AAIT
Groundwater potential assessment and characterization of Genale-Dawa River basin
By: Nebiyou Kassahun
2015
[Type text]
I D N O GSR/2651/06
ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES FACULITY OF TECHNOLOGY
Groundwater potential Assesment and Characterization of Genale- Dawa river basin
A thesis submitted to the School of Graduate Studies of Addis Ababa University in partial fulfillment of the Degree of Masters of Science in Civil Engineering (Stream: Hydraulic Engineering) By Nebiyou kassahun
Approval by Board of Examiners
------Chairman (department of graduate committee) Signature
Dr.-Ing Mebruk Mohammed . Advisor Signature
Dr. Agizew Nigussie . . Internal Examiner Signature
Dr.-Ing. Asie Kemal Jabir . External Examiner Signature
Groundwater potential assessment and characterization of Genale-Dawa River basin
Abstract
Genale-Dawa River Basin is one of the largest basins in Ethiopia. It is one of the most drought prone regions in Ethiopia. As a result a search for alternative source of water has always been a major issue in the region. This study therefore, aims at characterizing and evaluating the ground water potential resource of Genale-Dawa basin. The results of this research ultimately contribute to development of better water resources potential management.
Delineation of the Genale-Dawa River basin was carried out first in order to define the problem domain of the model. This has resulted in 17860km2 area of the basin. This area was discretized to form a three dimensional. The discretized region has 19620 nodes, 17862 equilateral triangular elements of varying sizes with a maximum of 5km edge dimension and 2500km model thickness.
The conceptualization of the model was done by grouping the discretized region in to 56 geological classes based on previous geological survey of the basin. The equivalent porous medium modeling approach was used to represent the different geological classes in the basin. Moreover, 23 rain gauge stations were used to determine the areal precipitation over the basin. The model takes perennial rivers as constant head boundaries, the side and bottom geometric boundaries of the model as no flow boundaries and the recharge due to precipitation as specified flow boundary.
After conceptualization of the flow system was complete and numerical model developed, TAGSAC model manual calibration was done by seating hydraulic conductivity and percentage recharge as calibration parameters when calibration is complete. The result was evaluated quantitatively using average indicators (AM, RMS, MAE) and qualitatively by comparison of groundwater contour maps generated with recorded and simulated hydraulic head data.
The calibration model was then used to determine monthly groundwater table fluctuation which eventually enabled the estimation of groundwater recharge potential of the basin. Additionally, base flow separation of perennial rivers was done to determine the monthly excess flux from the aquifer system. By adding these two values the total replenishable groundwater was estimated to be 2.78BMC. Hydro-geological map was also prepared based on hydraulic conductivity values obtained from model calibration. Identification of major groundwater recharge and discharge areas have also been done as an attempt towards basic groundwater flow system characterization. By Nebiyou k. Page ii
Groundwater potential assessment and characterization of Genale-Dawa River basin
Key words:
Ethiopia; Genale Dawa River Basin; Numerical Groundwater modeling; Replenishable Groundwater Potential; TAGSAC
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Groundwater potential assessment and characterization of Genale-Dawa River basin
Acknowledgment
First, I would like thank Debremarkos University for granting me the scholarship to study in Addis Ababa University and giving me a paid leave of absence during the time of my study and research. Most of all, my greatest appreciation goes to my advisor, Dr. Mebruk Mohammed, who invested his time, knowledge and energy throughout the whole research work. He is very supportive, willing, and hard working; in generally he has been an inspiration to me professionally. My deepest gratitude also goes to my family members who has encouraged me and supported me in ideas to the completion of my work. Furthermore, I would also like to express my warmest gratitude to Water Works Design and Supervision Enterprise, Ethiopian Ministry of Water Resources and Energy, and National Metrological Agency for their collaboration during secondary data collection. Lastly I want to thank friends, who encouraged and supported me to finish this research.
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Groundwater potential assessment and characterization of Genale-Dawa River basin
Table of Contents
Abstract ...... ii
Acknowledgment ...... iv
Table of Contents ...... v
List of Figures and Tables ...... viii
1. Introduction ...... 1
1.1. Statement of the problem ...... 2
1.2. Objective of the study ...... 2
1.2.1. Specific objectives ...... 3
2. Literature review ...... 4
2.1. Description of the area...... 4
2.2. Hydrology and Climate ...... 2
2.3. Geology ...... 5
2.4. Previous work ...... 6
2.5. Groundwater flow Model formulation ...... 8
2.5.1. Physical model ...... 8
2.5.2. Analog models ...... 8
2.5.3. Mathematical models ...... 9
2.5.3.1. Governing equations for saturated ground water flow ...... 10
2.5.4. Analytical modeling ...... 15
2.5.5. Numerical modeling ...... 15
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Groundwater potential assessment and characterization of Genale-Dawa River basin
2.5.5.1. Finite-difference method (FDM) ...... 16
2.5.5.2. Finite-element method (FEM) ...... 19
3. Methodology ...... 25
3.1. Data collection ...... 25
3.2. Numerical solution technique ...... 26
3.3. Spatial discretization ...... 27
3.4. Conceptual model ...... 30
3.5. Model calibration ...... 33
3.6. Estimation of Groundwater potential ...... 36
4. Results and discussion...... 38
4.1. Water point inventory data ...... 38
4.2. Rainfall distribution ...... 39
4.3. Base flow separation ...... 41
4.4. Flow system boundary ...... 43
4.5. Model calibration ...... 44
5. Conclusion and Recommendation ...... 56
5.1. Conclusion ...... 56
5.2. Recommendation ...... 57
Reference ...... 58
Appendix 1 ...... 61
Continuous Base flow Separation Method ...... 61
Appendix 2 ...... 62
Water point calibration data ...... 62
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Appendix 3 ...... 70
Geological coding ...... 70
Appendix 4 ...... 72
Mat lab Code for filling missing rainfall data ...... 72
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List of Figures and Tables
Figure 1 Location of Genale-Dawa river basin ...... 4 Figure 2 Isohytal map of Genale-Dawa river basin ...... 2 Figure 3 Rain fall types of Genale-Dawa river basin ...... 3 Figure 4 Geological Classes of Genale Dawa basin ...... 5 Figure 5 Control volume for groundwater flow through porous media ...... 10 Figure 6 Delineated DEM (Digital Elevation Model) of Genale-Dawa Basin Elevation ranges are shown in color bar ...... 29 Figure 7 triangularly discretized region of Genale-Dawa River Basin ...... 29 Figure 8 flow diagram representation of model calibration protocol ...... 37 Figure 9 Water point Distribution in Genale Dawa Basin ...... 39 Figure 10 Thiessen polygon diagram generated on Genale-Dawa Basin ...... 40 Figure 11 Flow system Boundaries ...... 44 Figure 12 Evaluation of calibration results using scatter plot between hs and hm 45 Figure 13 Ground water contour map generated with recorded hydraulic head 47 Figure 14 Groundwater contour map generated with simulated head ...... 48 Figure 15 mean monthly water table fluctuation ...... 51 Figure 16 Hydro geologic map of Genale Dawa basin ...... 53 Figure 17 Relationship between elevation of ground surface and water table ... 54 Figure 18 Identification of Recharging and Discharging areas In Genale Dawa Basin ...... 55
Table 1 Location and average precipitation of rainfall gauging stations used for areal rainfall calculation ...... 41 Table 2 monthly base flow contribution at gauging stations ...... 42 Table 3 Hydraulic conductivity values of different geologic medium on Genale- Dawa Basin (Geologic coding is presented in appendix 3 and is consistent with fig 4) ...... 49 Table 4 Total Replenishable Ground Water Calculation ...... 52
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1. Introduction
Groundwater is an important natural resource Worldwide. More than 2 billion people depend on groundwater for their daily supply (Kemper, 2004). It has been estimated that between one third and one half a billion people in Sub-Saharan African countries use both protected and unprotected groundwater for their daily water supply. Provided that the initial cost of well development is in a reasonable order, ground water based water work projects are always preferable. This is because; Ground water is abundant relative to surface water, dependable in the sense of amount and usually smaller cost for treatment plant is needed in case where water is used for domestic supply.
Ethiopia, being one of the most hydrologically blessed countries in east Africa, is believed to have a large ground water potential. Studies show erroneous results of 2.5 BCM by WAPCOS, to 185 BCM by Ayenew and Alemayehu, in 2001 (Moges, 2012). Which can be taken as an indication of how much detailed study and survey is needed to estimate the countries resources with a better precision. This ambiguity in estimation can have a hindering effect on the countries pursuit to utilize its water resources potential to the limit.
The country’s water supply coverage was estimated to be 30.9 percent, the rural water supply coverage being 23.1 percent and that of urban being 74.4 percent (Semu, 2012). Unpublished reports indicate that susceptibility to drought is higher in the periphery basins of the country such as Genale-Dawa than the central highlands due to high temporal variations of hydrological trends, making it hard to attain sustainable water supply in the region. Moreover, Master Plan Studies carried out during 1997-2007, indicates that Ethiopia has an estimated total potential irrigable land of 3,798,782 ha out of which 1,074,720 ha or 28.3% of the total irrigable land is in the Genale-Dawa River basin (MOWR, Integrated River Basin Master Plan Studies, carrried out during, 2007) Therefore, it can be drawn from the discussion above that, exploring sustainable and drought proof water resource is of significant importance. As an attempt to contribute to a suitable solution, this study focuses on evaluating the Genale-Dawa water resource potential and basic characterization of the ground water system. The study employs 3-D numerical ground water model to determine the monthly average groundwater table fluctuation, which then is used to determine the amount of recharge / replenishable ground water potential. The result obtained is then combined with the result of groundwater potential result by base flow separation approach.
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Groundwater potential assessment and characterization of Genale-Dawa River basin
1.1. Statement of the problem
Ethiopia has suffered from repeated drought scenarios in the past; especially the peripheries of the country like Genale-Dawa basin are more prone to drought than the interior highlands. At driest seasons even major surface water sources dry up, as a result the available large areas of suitable irrigation land are left uncultivated and in times, standard domestic water supply become scarce. As a result proper management and utilization of water resource is vital in the region. In the past, studies have been done on the region to estimate the water resource potential. However, even though an estimation of groundwater resource was done based on different basic approaches in the region, basin wise groundwater numerical modeling has not been done for the Genale-Dawa catchment. Numerical modeling however is an effective approach to groundwater potential estimation and also reveals basic characteristics of the flow system. This can be of significant importance for the detailed understanding of available water resources and can contribute to the betterment of water resources planning and management. This study therefore attempts to produce a research output that can be useful for sustainable use of available groundwater resource.
1.2. Objective of the study
The main objective of this study is to numerically model the ground water flow system of the study area. There by advance towards detailed understanding of hydro-geological components of the basin, this can eventually lead to:
Closely approximate the Genale-Dawa river basin ground water potential or replenishable recharge.
Hydro-geologically characterize the Genale-Dawa Ground water system.
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1.2.1. Specific objectives
Closely approximate the hydraulic conductivity characteristics and percentage recharge of the geological classes by performing model calibration. Determine seasonal groundwater table fluctuation that will be used for estimation of groundwater potential Determine base flow contribution of the groundwater flow system to nearby rivers by doing the necessary data checks, data fill and performing base flow separation. Hydro-geologically classify the aquifer system based on hydraulic conductivity values obtained from calibration.
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2. Literature review
2.1. Description of the area
The Genale-Dawa river basin is located in Ethiopia in the southern part of the country adjoining Kenya and Somalia international borders and is bounded by 3° 40’ N and 7° 43‘N latitudes and 37° 04’ E and 43° 28’ E longitudes. It is the third largest basin of the country after Abay and Wabishebele river basins covering an estimated area of about 176705km2 (MOWR, Genale Dawa River basin intigrated resources development master plan study hydrology sector, 2007). It encompasses the western half of Bale (South of Goba) and south-east, south-western and north-eastern parts of Sidamo
Figure 1 Location of Genale-Dawa river basin (Source Integrated Water Resources Development Master Plan Study)
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The catchment constitutes three river systems namely Dawa, Genale and Wabi Gastro. The Genale River is joined by Dawa River to form the Genale – Dawa River at the lower portion of the basin before crossing Ethio-Somali border which drains the western segment of the basin that is aligned with Omo-gibe river basin. Whereas north-eastern part of the basin is drained by the Weyeb -Gastro River that meets the Genale – Dawa River near the Ethio-Somalia border to form the Jubbah River that flows to the Indian ocean (Ethiopian National Meteorological Agency, 2013).
The southern part of the Southeastern Escarpment of the Main Ethiopia Rift Valley, Bale and Borena Highlands mark the main head waters of the Genale-Dawa River basin, that forms the water divide between the Mediterranean and Indian Ocean (Alemayehu, 2006). Altitude decreases from north to south and from west to east, this variation in altitude ranges in elevation from more than 4270m.a.m.s.l on the Bale Highlands to less than 173m.a.m.s.l near the international borders with Somalia and Kenya (south-eastern part of the catchment). Some 20% of the total area lies in the highlands above 1500m and 16% in the lowland plains below 500m (Master plan). The respective sub-basins of the Genale, Dawa and Weyeb Rivers occupy approximately 33%, 28% and 14% of the total Basin area. The remaining 25% is covered by the south and eastern border regions, drained by a number of intermittent streams which do not enter the main river systems (MOWR, Genale Dawa River basin intigrated resources development master plan study hydro-Geology sector, 2007).
It is mentioned in the integrated master plan that the Genale-Dawa basin area, as pre- defined by the MOWR and as shown in the previous figure, does not conform to strict hydrological divisions in the south-west and south-east. This is most apparent on the extreme south-eastern border in which a sizeable area is assigned to the Wabi-Shebele basin which actually drains into the Juba River in Genale Dawa basin. A corrected delineation of the basin is presented on fig. 6 of this study.
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2.2. Hydrology and Climate
The basin falls mainly in the arid and semi-arid zone and is generally drought-prone with erratic rainfall of an average monthly rainfall spacial variation ranging from 34mm to 143mm (Ethiopian National Meteorological Agency, 2013).
Figure 2 Isohytal map of Genale-Dawa river basin (Source Integrated Resources Development Master Plan Study)
The temporal variation of Hydrologic characteristics can mainly be described in relation with migration of the Inter tropical convergence Zone (ITCZ) as briefly described by Camacho, 1977. A brief review of his work by MOWR master plan describes that seasonal migration of (ITCZ), which is conditioned by the convergence of trade winds of the northern and southern hemisphere and the associated atmospheric circulation. It is also highly influenced, regionally and locally, by the complex topography of the basin, these accounts for the seasonal climatic changes.
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Classifications of rainfall regions based on the seasonal variation of monthly cumulative rainfall (rainfall type) is also described as follows
Figure 3 Rain fall types of Genale-Dawa river basin (Source Integrated Resources Development Master Plan Study)
Mono-modal: The area designated as region B on Figure 3. is dominated by a single Peak rainfall pattern in which the relative length of the wet period decrease in a north direction. Three sub-divisions B1, B2 and B3 have been defined according to duration of wet period from February/March to October/November and from June/July to August/September respectively.
Bi-modal Type I: The area designated as region A on Figure 3. is characterised by a quasi- double peak rainfall pattern with a small peak in April and maximum peak in August.
This region is therefore characterised by a semi-bi-modal rainfall pattern.
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Bi-modal Type II: The area identified as region C on Figure 3. is dominated by a double peak rainfall pattern with similar peaks during April and October. Generally, the annual rainfall decreases from west to east in the region.
Diffused pattern: The area designated as region D (Danakil region) is characterised by an irregular rainfall pattern. Though erratic rainfall occurs through the period from August/September to January/February, the pattern is diffused and not well-defined (MOWR, Genale Dawa River basin intigrated resources development master plan study, hydrology sector,, 2007).
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2.3. Geology
Physiographically the Genale Dawa basin is characterized by low relief tabular plateau made of Mesozoic sediments separated by deep river incisions. The geology of the Genale-Dawa River Basin were categorized into four major divisions. These are: (i) Precambrian crystalline basement, (ii) Late Paleozoic to Mesozoic sedimentary successions, (iii) Tertiary volcanic successions and (iv) Quaternary volcanic rocks and unconsolidated alluvial deposits. These major devisions were further classified and categorized in to 56 geological classes on the bases of geological discontinuities and tectonic bases (MOWR, Genale Dawa River basin intigrated resources development master plan study, Geology sector, 2007).
Figure 4 Geological Classes of Genale Dawa basin (see Appendix 3 for description of the classes)
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2.4. Previous work
Several studies were conducted on parts of the basin to generate the geological and Hydro geological maps on parts of Genale-Dawa basin with different resolution. In parts where detailed study was needed to identify a well field, electrical resistivity method and GIS and remote sensing based techniques were devised.
- Alebachew Beyene, Yetnayet Nigussie and Zenaw Tesema in 1987 Prepared Hydro- geology map of Upper Dawa Basin mainly based on Land seat interpretation with a scale of 1:500,000. (Beyene, Nigussie, & Tesema, 1987)
- Subsequent to the regional hydrogeological mapping, regional and detailed geo-physical surveys were conducted `in the Moyale area by (Hailemariam, 1990). This Survey was also done in the EIGS.
- Regional hydrological and geological works were done in the part at 1:5000000 by ICT Netherlands students as part of an exercise for their advanced diploma. The ICT students conducted their Hydro-geological studies by means of aerial photograph and satellite image interpretation as well as field visits. They have indicated the potential aquifer sites for ground water development options. (MOWR, Genale Dawa River basin intigrated resources development master plan study, 2007)
- Ground water potential mapping of Yabelo, a sub catchment of the Genale-Dawa river basin was carried out to a scale of 1:135000 based on GIS and remote sensing by (Mab consult – consulting hydro-geologists, 2007)
- Genale-Dawa River Basin Integrated Resources Development Master Plan Study (2000) was done by MOWE which is detailed basin wise study that briefly covers the geologic and hydro geologic aspects of the basin at large. This work resulted in generating the hydro-geological mapping of the basing along with classification of aquifer productivity based on hydro-geologic features like transitivity and extent of geologic media,
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therefore potential water resources development sites were identified ( (MOWR, Genale-Dawa River Basin Integrated Resources Development Master Plan Study, 2007)).
- The national water resources master plan employed 3 methods for ground water potential assessment of Genale-Dawa basin namely, Sub surface drainage Approach, Recharge area approach and Base flow approach was used and ground water potential of 1.78 BM3 , 0.43BM3 , 0.5 BM3 were obtained respectively (WAPCOS, 2007).
- Hydro-geophysical surveys were conducted by Aklilu et.al and Hailu et.al around Negele and Filtu towns in 2001. A geophysical method which is VES (Vertical Electrical Sounding) using schlumberger array was employed to acquire the necessary subsurface electrical information with 330m minimum AB/2 separation. Consequently areas which have favorable conditions for groundwater presence were identified around the two towns (Aklilu & Hilu, 2001).
- Geological and hydro-geological maps of Asela sheet which covers part of the Genale- Dawa river basin was prepared by (kiflu, tafa, & mulugeta, 2001) to the scale of 1:250,000 with an accompanying report based on both the geological and hydrogeological information gained during the whole ground water resource assessment. On the basis of this aquifer systems of the area have been defined and characterized.
- Other geological sheets have also been investigated in the Genale-Dawa basin previously. However, Basin wise Numerical models on the Genale-Dawa basin were not encountered for literature review. Most of the previous works conducted on the catchment focus mainly on geological and hydro geological mapping of the area in different scales and resolutions. These can be regarded as a direct approach towards ground water potential assessment. However, this previous studies have contributed in raw data and were basic input for conceptualization of current basin wise model development.
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2.5. Groundwater flow Model formulation
A model is a tool designed to represent a simplified version of reality which can be represented either physically or abstract to capture the significant features of a system. Several types of groundwater models have been used to study ground water flow systems. They can be divided into three Broad categories (prickett, 1975): physical models, analog models, including viscous fluid models and electrical models, and mathematical models, including analytical and numerical models.
2.5.1. Physical model
Sand tank is the most common type of physical model. In sand tank model, the actual field dimension was scaled down (three-dimensional) to the laboratory scale and the appropriate aquifer materials are introduced in the box and the model is simulated by incorporating appropriate pumping of water from the model and injection of water in to the model and with appropriate boundary conditions (Thangarajan, Groundwater, Resource Evaluation, Augmentation, Contamination,Restoration, Modeling and Management, 2007). The major drawback of sand tank models is the problem of scaling down a field situation to the dimension of laboratory model. (Herbert F Wang, 1982).
2.5.2. Analog models
An analog model utilizes the similarity of the two physical systems and the one, which is easier to handle, is used as a model of the other. For example mathematical governing equations of the physical processes such as flow of electrical current through resistive media or flow of heat through a solid body are analog physical processes that can be used to model ground water flow. Viscous Fluid Models, Electric Analog Models, Resistance-Capacitance Analog Modeling are some of the common Analog models used for ground water modeling. (Thangarajan, Groundwater, Resource Evaluation, Augmentation, Contamination, Restoration, Modeling and Management, 2007))
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2.5.3. Mathematical models
Mathematical models are abstractions that represent processes as system of equations, physical properties as constants or coefficients in the equations, and measures of state or potential in the system as variables. (Delleur, 1999).
Depending up on the nature of equations involved mathematical models can further be divided in to:
Empirical (experimental): empirical models are derived from experimental data that are fitted to some mathematical function. (a good example is Darcy’s law)
Probabilistic: probabilistic models are based on laws of probability and statistics. They can have various forms and complexity starting with a simple probability distribution of a hydro geological property of inters, and ending with complicated stochastic, distribution of a hydro geological property of interest and ending with complicated stochastic, time-dependent models. The main limitations for a wider use of probabilistic models in hydrogeology are: (1) they require large data sets needed for parameter identification and (2) they cannot be used to answer the most common questions from hydro geological point of view
Deterministic: Deterministic models assume that the stage or future reactions of the system studied are predetermined by set of physical laws governing the flow (Anderson & Woessner, 1992).
In the deterministic approach one can see the derivation of groundwater flow governing equation.
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2.5.3.1. Governing equations for saturated ground water flow
Consider a unit volume of saturated porous media in Fig4. In fluid mechanics, such a volume is called a control volume. The boundaries of the element are called control surfaces.
Figure 5 Control volume for groundwater flow through porous media Source ( Istok, 1989).
The law of the conservation of mass states that the sum of the gains or losses of mass flow in the X, Y, and Z directions is equal to the loss or gain in mass of the groundwater stored in the elemental control volume Per unit time. For purposes of analysis, consider the rate at which groundwater enters the control volume per unit surface area to consist of three components rυx , rυy and rυz where r is the density of water and υx, υYand υZare the apparent velocities of groundwater flow entering the control volume through control surfaces perpendicular to the x, y, and z coordinate axes- The dimensions of rυx, rυy, and rυz are ML2/T.
Using a Taylor Series approximation, the rate at which groundwater leaves the control volume in the x direction can be written.
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υ υ υ υ +……
If we make the size of the control volume small, we can neglect higher-order terms (i.e, those involving , , etc) and because we have chosen a unit volume = =1 the net υ rate of inflow at the x direction is υ . The net rate of inflow in the x direction is then
Net rate of inflow = rate of inflow in x direction - rate of outflow in x direction
υ = υ – [ υ ]
υ =
υ υ And the net rate of inflow in the y and z directions are and respectively.
According to law of conservation the net rate of inflow or outflow for the entire control volume must equal to the net change in mass of the control volume.
υ υ υ ……… (1)
If we assume that groundwater density, p is constant specially and temporally (i.e., the fluid is incompressible), we can use the product rule of calculus to evaluate a typical term in the above equation
υ υ υ
But υ
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Therefore υ υ
Doing similar simplifications for the time component, y direction and z direction and canceling density that appears outside the derivative (eq. 1) we have
υ υ υ
Now the apparent groundwater velocities are given by Darcy's Law
υ
υ
υ
Where , and are the hydraulic conductivities in the x, y, and z directions, respectively and h is the hydraulic head. Substituting equation AI.7 into equation AI.6. and including recharge We arrive at the steady-state, saturated/low equation.
If an aquifer parameter is assumed to be homogeneous (at list with in a finite element in case of numerical modeling), then the chain rule can be employed to get a further simplified equation
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(Jonathan I stole, 1989)
Aside from heterogeneity, that is, porosity and conductivity variations from point to point, a dependence of hydraulic conductivity on direction is possible. This is the case for the so-called anisotropic porous media, where due to some direction-related properties, as preferential lining of fractures, stratifications, or layering, the conductivity changes depending upon direction. Such situations can be described by an extension of previous equations, where the conductivity becomes a symmetrical matrix (ie. Conductivity tensor), K, with the following components (Jacques W. Delleur, the hand book of ground water engineering)
Therefore the most general form of Darcy’s law can be written as
υ
υ
υ
In case the 3 coordinate axes coincide with the principal axes of the hydraulic conductivity tensor (the direction of maximum, minimum, and intermediate hydraulic conductivity), K becomes a diagonal tensor and the Darcy's law thus is simplified (Zehang, 2011).
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υ
υ
υ
And the most general form of steady state Laplace’s equation in this case can be written In matrix form as:
Assumptions
Some of the simplifying assumptions in steady state groundwater flow equation are made are:
Aquifer medium is assumed to be incompressible Ground completely saturated No changes in hydraulic conductivity and piezometric head as a function of time. Darcys flow equation is assumed to be valid and flow to be laminar Flow is assumed to be slightly compressible under high pressure
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Limitations
The major limitations of this study include:
The model is tailored to isothermal fully saturated fractured porous medium systems (solves the Richard’s equation).
In performing a saturated flow analysis, the study handles only single-phase flow of the liquid (i.e., water) and ignores the flow effects from other potential phases (i.e., air or other non-aqueous phases) which, in some instances, can be significant.
Deterministic mathematical models of ground water flow problems usually involve partial differential equations which need to be solved by either analytical or numerical methods.
2.5.4. Analytical modeling
An analytical model aims at obtaining an exact solution of a mathematical description of a physical process. However, groundwater flow equation, which could be amenable to analytical techniques, requires several simplifying assumptions of the system including the boundary and initial conditions. It also requires large computational resource. This process usually renders the system under study far from being realistic ( (Thangarajan, Groundwater, Resource Evaluation, Augmentation, Contamination, Restoration, Modeling and Management, 2007)). This method is usually difficult to employ for large scale groundwater modeling owing to its need for substantial computational resource
2.5.5. Numerical modeling
Numerical modeling employs approximate methods to solve the partial differential equation (PDE), which describes the flow in porous medium. The emphasis here is not on obtaining an exact solution but on obtaining reasonably approximate solution. (Thangarajan, Groundwater, Resource Evaluation, Augmentation, Contamination,Restoration, Modeling and Management,
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2007). Numerical modeling is not subject to many of the restrictive assumptions required for familiar analytical solutions. Numerical solution normally involves approximating continuous (defined at every point) partial differential equations with a set of discrete equations in time (transient model) and space (steady state model). Thus, the region and time period of interest are divided in some fashion, resulting in an equation or set of equations for each sub region and time step. These discrete equations are combined to form a system of algebraic equations that must be solved for specified points in the solution region. Finite-difference and finite-element methods are the major numerical techniques used in ground water applications the two methods are presented as follows (Faust & Mercer, 2006).
2.5.5.1. Finite-difference method (FDM)
The finite difference method consists of discretising the problem area into rectangular elements which are identified with discrete points or nodes ( (Essink, 2000)). Various hydro- geological parameters are assigned to each of these nodes. Accordingly, difference operators defining the spatial-temporal relationships between various parameters replace the partial derivatives. A set of finite difference equations, one for each node is, thus, obtained. In order to solve a finite difference equation, one has to start with the initial distribution of heads and compute heads at later time instants ( (Thangarajan, Groundwater, Resource Evaluation, Augmentation, Contamination,Restoration, Modeling and Management, 2007)).
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The finite difference method is based on the Taylor’s series expansion and its most basic discretization approaches can be shown as follows
Taylor’s Series
If f(x) is an infinitely differentiable function then the Taylor Series of f(x) about x=x0 is,
Where:
is the nth derivative of the function f.
Known solution point (boundary condition)
X point of interest in the function
Forward in space expansion can be done as follows
If
+……
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Groundwater potential assessment and characterization of Genale-Dawa River basin
+……
For the sake of simplicity terms containing second order and higher derivatives are truncated.
...... (1)
Backward in space
If
+……
+……
Similar to previous one, terms containing second order and higher derivatives are truncated.
…………. (2)
Central in space
+……
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Groundwater potential assessment and characterization of Genale-Dawa River basin
+……
Terms of third order derivative and higher are truncated.
……….. (3)
These last expressions in the forward backward and central expansions can be used to approximate a function by forming a system of polynomial equations that can be solved using matrix solution techniques. From the above calculation it can be seen that central in space approximation has smaller truncation error hence a better approximation. The solution of the groundwater problem can be found, by simultaneously solving the sets of algebraic equations of the aquifer at discreet points.
2.5.5.2. Finite-element method (FEM)
The finite element method (FEM) is a very well-known method to solve the governing partial differential equations (Essink, 2000).The basic idea in the finite element method is to find the solution of a complicated problem by replacing it with a simpler one. The solution region is considered as built up of many small, interconnected sub regions called finite elements. The first step of the finite element analysis involves the discretization of the irregular domain into smaller and regular sub domains, finite elements. This is equivalent to replacing the domain having an infinite number of degrees of freedom by a system having finite number of degrees of freedom. The shapes, sizes, number, and configurations of the elements have to be chosen carefully such that the original body or domain is simulated as closely as possible without increasing the computational effort needed for the solution. Mostly the choice of the type of element is dictated by the geometry of the body and the number of independent coordinates necessary to describe the system (Rao, 2005) for one-dimensional problems, the elements are lines; for two dimensional problems, the elements may be either triangles or quadrilaterals; and for three dimensions, they are tetrahedrons or prisms (Charles R. & Mercer, 2006)In groundwater problems, the polygonal
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Groundwater potential assessment and characterization of Genale-Dawa River basin shape of the element is almost always triangular (in two-dimension triangles), whereas occasionally more complex quadrilaterals are used. An irregular polygonal mesh allows the modeler to follow the natural shapes more accurately (Essink, 2000).
Simpler polynomial approximation equations called basis functions are used to determine field variables within a finite element. For the finite element method an integral approach (instead of a differential approach as in the finite difference method) is applied. Two main solution principles of the finite element method can be distinguished (1) the variation principle (using so-called functionals) and (2) the weighted residual technique which mostly preferable for its simplicity. One of the most popular weighted residual techniques for a groundwater problem is the Galerkien’s method (Essink, 2000).
Application of the Galarkine’s method for the generation of approximating system of equations for a three dimensional finite element method can be demonstrated as follows:
Where L is the differential operator, h is the field variable (Hydraulic head), and F is a known function. Define an approximate solution h of the form
Where Ni are interpolation functions, h are the (unknown) values of the field variable at the nodes, i refers to a particular node and m is the total number of nodes in the mesh. If the approximate solution is substituted back in to the differential operator the equation is no longer satisfied.
Where R is the residual or error due to the approximate solution. The residual varies from point-to-point within the problem domain. According to the Garerkine’s method the residuals at different points is normalized by weighting it with the interpolation function.
Where represents the problem domain
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Groundwater potential assessment and characterization of Genale-Dawa River basin
To evaluate the above equation, we must specify the mathematical form of the approximate solution h and the weighting function W. In the finite element method h is defined in a piece - wise fashion over the problem domain.
‘R’ represents the error between the true value of hydraulic head and the approximate solution h at that node. The residual at a particular node is the sum of weighted residuals of neighboring nodes.
The contribution of element e to the residual at node i can be obtained from the integral formulation for that node. Consider one dimensional case in the x direction with two nodes i and j
Because the second derivation of a linear interpolation function which is common, is undefined; h expression of the equation in terms of first derivative ) is needed. The Green’s theorem can be applied. Negative sign is added for convenience in letter calculation.
The second term in the above equation is given a symbol for an element and represents groundwater flow across the element's surface. At the exterior of the mesh this expression represents rates of boundary condition. Where no flows are specified or at impermeable aquifer boundaries, e) will be zero. For elements on the interior of the mesh, the term for adjacent elements will have opposite signs cancelling out the contribution of for the
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Groundwater potential assessment and characterization of Genale-Dawa River basin neighboring elements for the node(s) they share. Hence omitting the second term in the right hand side of the equation
Recall groundwater governing equation described in the previous section, it can be written in terms of h
Applying the galarkine’s method
To understand the solution approach to this equation lets first consider a one dimensional flow with two nodes. Where, and represent the two nodal coordinates used to define the element. Because each element had two nodes, it contributed to the residual at two nodes, Rj and Ri. Represent these residuals as separate integral equations and we can write;
Substitute and rewrite in matrix form the conductance matrix can be written
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Groundwater potential assessment and characterization of Genale-Dawa River basin
Similarly, for a 3 dimensional equation with n number of nodes we have
Conductance matrix is used to write the system of equations for an element
These equations of individual elements are then combined to form the global matrix
In order to incorporate Boundary conditions, the integration of q (recharge) term from the general ground water flow equation above is considered.
q (e) represents a specified flow rate along the boundary of element e. And from Grean’s theorem, it can also be write as
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Groundwater potential assessment and characterization of Genale-Dawa River basin
Where is the surface area of element e. The evaluation of these integrals for each node in element e gives the components of the specified flow matrix for element e, { }
And incorporated in the matrix form as follows depending on the hydraulic gradient sign
We can combine the for each element in the mesh to obtain the global specified flow matrix
The most general form of system of equation that represents the saturated subsurface flow system by setting can be written as
This is usually a sparse matrix and is solved with a matrix iterative solution methods (Istoke, 1989)
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Groundwater potential assessment and characterization of Genale-Dawa River basin
3. Methodology
Assessment of ground water potential can be achieved through various methodologies. This chapter focuses on explaining how the FEM modeling approach was adopted for the regional groundwater potential assessment of the current study area.
3.1. Data collection
Various relevant raw data that can reveal an insight of the subsurface reservoir and softwares useful for modeling were collected. These are:
Softwares used for the model development, including;
Mat lab v.13a: where numerical calculations are carried out and the TAGSAC code is run. Global Mapper v.16, Surfer v.10: used for surveying works, delineation, digitization, data manipulation and data pre processing.
Hydrogeologic data
1:2000, 000 resolution geological map with 56 geological classes were collected. Springs and well inventory data. 30×30m resolution Digital elevation model of the region in which Genale-Dawa River basin is located.
Hydrologic data
Rain fall data records of 23 gauging stations near and on the basin.
Stream flow data of some gauging station on the basin; this was used to develop an understanding of the overall hydro geological system and also, determine aquifer contribution to the rivers by performing base flow separation.
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3.2. Numerical solution technique
Laplace’s equation as described in the previous chapter is a general equation that governs groundwater flow system and other analogous systems like conductance of heat with in a solid body. In mathematical modeling of groundwater this equation needs to be solved either analytically or numerically. Analytical solutions can be used to calculate values for the unknown field variable at any point in the problem domain. Whereas, numerical solutions yield values for only a predetermined finite number of points in the problem domain. Considering the complexity of the problem; the numerical method is chosen for this study. And from the two well known numerical methods available the (finite element and finite difference) the finite element is selected considering its computational efficiency. This is due to the following reasons:
1) FEM is more suited to better discretising a given solution region owing to the use of unlimited discretising element shape and size.
2) Because different type and degree of approximation equations can be used, it can better approximate the solution compared to the FDM, which can be considered as a method that uses linear interpolation between two points towards determining field variable at succeeding discrete points. (The Finite Element Method, O.C. Zienkiewicz and R.L. Tylor, 2000, by butterworth-Heinemann, England)
A particular type of FEM based three dimensional computer modeling code called TAGSAC (Three Dimensional Analysis of Groundwater Flow, Saitama University Code) is adopted for modeling the groundwater system. TAGSAC is a model developed for the porous medium. In the TAGSAC approximation procedure, the flow region is first discretized into a network of finite elements, and then trial approximating interpolation functions are generated for individual finite elements using a special type of weighted residual method called Galerkin’s method; in which the summation of residuals weighted by interpolation functions is equated to zero. This results in a system of linear interpolation functions. By incorporating boundary conditions and solving, coefficients of interpolation functions are obtained. This system of equations is used to represent the unknown dependent variable (hydraulic head) over the discretized region. A limiting feature of TAGSAC model is that it is bound to use a model thickness not less than half the finite elements dimension used; which otherwise will risk
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Groundwater potential assessment and characterization of Genale-Dawa River basin numerical instability. Tagsac Has Proved To Be Applicable In A Number Of Researches Done All Over The Globe (Mohammed, 2010).
3.3. Spatial discretization
The first step of the finite element analysis involves discretization of the irregular domain into smaller and regular sub domains. This is equivalent to replacing the domain having an infinite number of degrees of freedom by a system having finite number of degrees of freedom. The following section is dedicated to describe the discretization processes.
The geometric representation of the system shall first be established for which, Cartesian coordinate system is employed to generate a triangular in plane three dimensional mesh. The x- y plane coincides with plane view of the study area where as z direction point’s perpendicularly in the upward direction to the x-y plane, representing elevation.
An initial step taken for discretization was to delineate the Genale-Dawa catchment area which was done using Global Mapper version 16 and using digital elevation model of 30×30m resolution. The result obtained was a little different than the official delineated map of Genale- Dawa basin used by MOWE in that the delineation result obtained and to be used for this study has some additional area on the south-eastern part of the basin with a total of 17860km2 km2. This, in recent master plan study of the basin was recognized as covered in the literature review part of this paper.
After the delineation x-y coordinates of the catchment boundary are generated and used as problem domain of the model. The discretization elements are made to be non uniform in size to best fit the boundaries of the problem domain where there are Sharpe corners. A maximum of 5km edge dimension for the equilateral triangular finite element is selected. This done considering the available computational capacity and level of details of raw data available. The problem domain of 17860km2 area is then discretized in to 9810 nodes of known x-y coordinates with consistent and continuous nodal numbering assigned to them. 17862 triangular elements are formed by connecting three neighboring nodes with a line. This geometric discretization of the region is first done on x-y plane and is carried out using automatic discretising Mat lab computer code. The z coordinate of the nodal points of the mesh is tabulated by interpolation from the digital elevation model. After wards this triangular mesh so formed is given a model thickness of 2500m to form the three dimensionally discretized
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Groundwater potential assessment and characterization of Genale-Dawa River basin systems. Hence, each triangular two dimensional element was changed in to three dimensional finite element formed with six nodal points in space and becomes a prism that is triangular in plain. Boreholes and springs are represented by 3 nearest surface nodes where as River systems are made to traverse along a series of surface nodes; this is done by moving the nearest surface node to the river at a point using a mat lab code. Relocation of the top layer nodes near a river causes vertical distortion of the prismatic finite elements that can be handled by TAGSAC.
Finally, individual elements and surface nodal points are given codes that designate the material property and rainfall recharge amount respectively to the individual elements and surface nodes. Fig. 6 and Fig 7 Respectively shows the geometrically discretized domain and geologic materials assigned for each discreet triangular element by coloring the element centroid with different classes of colors.
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Groundwater potential assessment and characterization of Genale-Dawa River basin
Figure 6 Delineated DEM (Digital Elevation Model) of Genale-Dawa Basin Elevation ranges are shown in color bar Discretization
Figure 7 triangularly discretized region of Genale-Dawa River Basin
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3.4. Conceptual model
3.4.1. Conceptualization of flow medium
Several conceptual groundwater flow models have be distinguished on the basis of the storage and flow capabilities of the porous medium and fracture. The storage characteristics are associated with porosity, and the flow characteristics are associated with permeability. Three conceptual models have dominated the research: 1) dual continuum, 2) discrete fracture network, and 3) single equivalent continuum. In addition, Explicit discrete fracture, multiple- interacting continua and multi-porosity/multi-permeability conceptual models (Sahimi, 1995) have been introduced in the literature. For the sake of discussion the first 3 are presented below
Discrete Fracture Network
Discrete fracture network (DFN) models describe a class of dual-continuum models in which the porous medium is not represented. Instead, all flow is restricted to the fractures. This idealization reduces computational resource requirements. Fracture “legs” are often represented as lines or planes in two or three dimensions (Sarkar, Toksöz, & Burns). The DF approach is typically applied to fractured media with low primary permeability such as crystalline rock. (Anderson & Woessner, 1992).
Dual-continuum models
Dual-continuum models are based on an idealized flow medium consisting of a primary porosity created by deposition and lithification and a secondary porosity created by fracturing, jointing, or dissolution. The basis of these models is the observation that un-fractured rock masses account for much of the porosity (storage) of the medium, but little of the permeability (flow). Conversely, fractures may have negligible storage, but high permeability (Sarkar, Toksöz, & Burns).
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Single Equivalent Continuum Formulation
Fractured material is represented as an equivalent porous medium by replacing the primary and secondary porosity and hydraulic conductivity distributions with a continuous porous medium having so called equivalent hydraulic properties (Anderson & Woessner, 1992). This method is most suited to the condition in which the volume of interest is considered to be large enough that, on average, permeability is a sum of fracture and porous media permeability. This approximation substantially simplifies the flow problem (Diodato, 1994).
This study employs the single equivalent continuum conceptual modeling approach in which the hydraulic parameters are selected so that the; flow pattern in the discretized elements is similar to the flow pattern in the actual fractured system. This formulation methodology is adopted, taking in to account the sizes of the (Representative Elementary Volume) REV considered being large and the moderate availability of computational resources. (Istoke, 1989)
3.4.2. Ground water recharge
Recharge is defined as the downward flow of water reaching the water table forming an addition to the ground water reservoir (Vries & Simmer, 2000). It is also defined as a term used to describe many of the processes involved in the addition of water to the saturated zone (Moore & Wilson, 1998). When the front of infiltrating water reaches the capillary fringe (percolates), it displaces air in the pore spaces and causes the water table to rise along with the capillary fringe (Applied hydrology ground water).
Groundwater recharge rate, as briefly described by (Healy, 2010), is both specially and temporally varied. This variability is due to a number of factors such as; climate, soil cover, geology, surface topography, hydrology and vegetation cover. Therefore, a good recharge estimation for a given study area; requires a clear understanding of the factors in play for the specific site under study. This usually, is not an easy task to achieve because of both financial and techniqueal difficulties faced with. However, some methods such as Chemical tracer methods, Water-budget methods and numerical modeling methods in which Recharge estimates can be obtained through a model calibration process with recharge rate as a calibration parameter (Healy, 2010)can be used to get a close estimation.
Major natural recharge to the unconfined aquifer system in the current study area occurs at elevated regions due to percolation from precipitation along the north, north-eastern and north-western boundary highs of the basin. Whereas recharge from runoff and precipitation on the lower part of the basin also provides a source of groundwater inflow to the area of interest.
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Groundwater potential assessment and characterization of Genale-Dawa River basin
This study tries to estimate the replenishable groundwater resource (from hydrological perspective) using the TAGSAC model by seating precipitation to be some portion of the total rainfall in percent and making this percentage a calibration parameter that can be obtained through a series of trial and error procedure.
3.4.3. Model Boundary conditions
It is crucial to define a boundary condition prior to numerical groundwater model development. This is because, the solution of Laplace’s equation requires specification of boundary conditions which constrain the problem and make solutions unique (Anderson & Wosner, 1992).Hence, boundary conditions are known solutions at points in the solution domain necessary to obtain solution at unknown points representative of the real system. P.Anderson and W.Wosner have distinguished the different types of boundary condition.
Which are described as follows:
A) head is known for surfaces bounding the flow region (Dirichlet Conditions); B) flow is known across surfaces bounding the region (Newman condition) C) a combination of Dirichlet and Newman conditions known as mixed condition
The most common types of boundary conditions are; perennial rivers, springs, lakes and swampy areas known to have ground water reserve underneath, all of which can be taken as Dirichlet boundary conditions after a careful observation of their relation with the aquifer nearby and the hydraulic property of intermediate medium. On the other hand; known amount of inter-aquifer leakage, water wells and springs of known discharge can be taken as Newman’s conditions. The determination of which aspects of an actual ground-water system should be incorporated into a computer simulation usually depends, in part, upon the objectives of the study for which the model is being developed (Reilly, 2001) accordingly in constant head constant discharge and specified flow boundary conditions have been identified for the current modeling.
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3.4.4. Hydraulic properties
Hydraulic properties important for the three dimensional conceptual model include both horizontal and vertical hydraulic conductivities as well as specific storage coefficient. In order to generate the numerical model of the site, the distribution of these parameters must be specified for each hydro-geological unit. However since the model is based on the principles of equivalent porous medium, Hydraulic properties are assumed to be equivalent or effective values for the 56 individual geological class. These geological classes have been discretized and Equivalent hydraulic properties are obtained by calibration procedure for respective geological classes.
3.4.5. Water points inventory
Water point inventory data is comprehensive data collected about the water points. Inventory data of water points on the basin include data about water wells, hand dug wells and springs. The details of this data include static water level and coordinates of individual water wells and springs. Which is important for the model calibration process, as it is evident information available regarding the regional groundwater condition. The water point inventory data collection on the basin was carried out using dip meters and GPS instruments.
3.5. Model calibration
Model calibration is the process of adjusting the input properties and boundary conditions of a model to achieve a close fit to observed conditions in the real groundwater system. In flow model calibration, simulated heads and discharges are typically compared to their observed counterparts. If a model is well calibrated, there will be some random deviations between simulated and observed data, but there will not be systematic deviations. If there are systematic deviations such as most simulated heads exceeding observed heads, the calibration is poor and adjustments should be made (fitts, 2002).
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Groundwater potential assessment and characterization of Genale-Dawa River basin
As discussed by (Kresic, 2009) , there are two methods of calibration:
(1) Trial and error (manual) and
(2) Automated calibration.
In the trial and error approach, the user inputs all the parameters that can be based on physical observation, and provides estimates of the unknown parameters as a first trial. As such, the adjustment of parameters is manual. The model is run and the computed output is compared to the measured output from the model. Most of the time the transmissivities are the least known parameters and thus, they are often modified during the calibration procedure. The comparison is done either by means of visual pattern, or it is based on some mathematical criterion. Based on this comparison, adjustments are made to one or more of the trial parameters to improve the fit between measured and computed output. In an effort to get the best fit between measured and computed output, it helps to go back to the basic principle stated by Darcy in which the relationship between h ,k and q can be used as a rule of thumb in calibration. If for example the measured head is larger than the computed. As it can be seen from Darcy’s law; reducing the hydraulic conductivity in some proportion would result in larger head to be computed (close to the observed head) by the model and vice versa (fitts, 2002). Hence, a systematic trial and error (manual) calibration can be achieved. These trial runs of the model are repeated until some kind of required accuracy or calibration target is achieved (Essink, 2000) . Trial and error calibration was the first technique applied in groundwater modeling and is still preferred by most users (Kresic, 2009). Whereas on the other hand automated calibration method employs a computer program that will automatically calibrate itself and carry out the necessary number of trial runs until the best set of parameters is achieved. The purpose of this program is to minimize an objective function such as to minimize the sum of the square residuals. Though this approach is advantageous for that it gives statistical degree of uncertainty and saves time. It should also be kept in mind that it can also give unstable and unreasonable results. This paper employs the trial and error calibration procedure as described earlier. The protocol followed in the modeling and calibration is shown using a flow diagram described in figure 8.
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Groundwater potential assessment and characterization of Genale-Dawa River basin
3.5.1. Calibration Evaluation
The result of the calibration should be evaluated both qualitatively and quantitatively (Anderson & Wosner, 1992)
(a) Qualitatively, by comparison of contour maps of measured and computed parameters, which provides only a qualitative measure of the similarity between the patterns; and
(b) Quantitatively, by a scatter plot of measured and computed parameters, where the deviation of points from the straight line should be randomly distributed (Essink, 2000). In an effort to minimize the error in the calibration, the average deviation is calculated using the mean error (ME), mean absolute error (MAE) and root mean squared error (RMS) indicators, the calibration is continued until these indicators are satisfactorily minimized.
1. The mean error (ME)
2. The mean absolute error (MAE)
3. The root mean squared error (RMS)
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The maximum acceptable value of calibration criterion depends on the magnitude of the change in head over the problem domain (Anderson and Woessner, 1992). The scatter diagram generated by model also shows the matching property of the measured simulated head. The scatter plot is usually examined by the position of points scattered in the graph away from the straight line, that is; random distribution of point in the plot shows the deviation between measured and simulated groundwater heads.
3.5.2. Model calibration Target
A calibration target consists of the best estimate of a value of groundwater head or flow rate. Establishment of calibration targets and acceptable residuals or residual statistics depends on the degree of accuracy proposed for a particular model application. This, in turn, depends strongly upon the objectives of the modeling project (ASTEM, 2008). For any particular calibration target, the magnitude of the acceptable residual depends partly upon the magnitude of the error associated with data collection. Head measurements in particular are usually accurate to within a few tenths of a foot. Due to the many approximations employed in modeling and errors associated therewith, it is usually impossible to make a model reproduce all head measurements within the errors of measurement. This incompatibility can be adjusted by taking data collection error in to account and providing a range of acceptable errors for the model output.
As stated by ASTEM (D5981 – 96), the acceptable residual should be a small fraction of the difference between the highest and lowest heads across the site.
3.6. Estimation of Groundwater potential
After the model calibration, monthly water table fluctuation is calculated. This helps determinine the maximum water table, minimum water table and eventually the change in storage of the groundwater system within a year. This amount of water is known as the replenishable groundwater which represents the recharge capacity of the system. But, even though in a given month the aquifer is assumed to have a given amount of groundwater storage
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Groundwater potential assessment and characterization of Genale-Dawa River basin with an associated water table it also discharges to the rivers at the same time. Hence, base flow separation is done using digital filter method to account for the water at the discharging end of the system and the cumulative groundwater reserve is calculated for individual months.
Finally based on calibration results hydraulic conductivities of different geologic classes are grouped to generate the different hydro-geologic classes of the region and prepare Hydrogeologic map of the region.
Model protocol
All the discussion made in this chapter applies to the numerical model development of Genale Dawa River Basin. The model protocol followed and work frame adapted to modeling of the basin can be represented with a flow diagram as follows.
Field system
Conceptualization of Measured Field Genale-Dawa basin variable
Estimation of parameters and boundary condition
Numerical Model Error analysis using different Development Computed output indicators (RMS, RAM, RM, (field variable) Line graph, contour map (TAGSAC model) comparison)
Review on estimation of parameters and boundary Acceptable condition
Calibrated model
Unacceptable Figure 8 flow diagram representation of model calibration protocol
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4. Results and discussion
The methodology described in the previous chapter was employed to develop a three dimensional numerical single layered groundwater model for the evaluation of replenishable groundwater potential. Results and relevant discussions of the model are presented in this chapter in their chronological order.
4.1. Water point inventory data
Water point inventory data relevant for the model calibration was collected from concerned offices. This included; bore hole, hand dug well and spring data of Genale-Dawa River basin. Out of these collected data, some data was omitted for not qualifying to contain either the static water level or coordinate information. A secondary data screening was also done by comparing recorded static water level with expected result from the model. Accordingly, personal judgment was taken to discard where large data inconsistency is observed. After screening 82 Bore holes, 49 hand dug wells and 191 spring data were left to be used as an input for the model.
The distribution of water points used for model calibration is shown in figure 9. From the distribution of this water points it can be seen that more water points are located in the northern north-western and north-eastern parts of the basin which in general are topographically elevated areas. These parts of the basin are also the ones that receive majority of the precipitation and consequently majority of the recharge in the basin. On the other hand it can be seen that there is less concentration in the central and south-western parts of the basin. Therefore, even thought there is uniformity absence in the distribution of water point inventory data for an ideal model calibration use, the fact that the distribution of water well data tends to be concentrated on parts of major recharging areas of the basin is fortunate and has a positive effect in capturing and conceptualizing the main features of the groundwater flow system for recharge potential estimation.
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Bore hole Hand dug well Spring
Figure 9 Water point Distribution in Genale Dawa Basin
4.2. Rainfall distribution
Determination of rainfall distribution over the basin is important prior to modeling in the groundwater potential assessment. It is used to approximate the amount of recharge from precipitation. Hence, Precipitation data of the study area was collected from Ethiopian National metrological Agency with a maximum of 15 years and a minimum of 10 years record. Data filling was done where there is missing. This was accomplished using a math lab program that uses the inverse distance method which was developed and coded by the authors of this study. The program is capable of dealing with a large amount of data; the Mat lab code of this program is attached in Appendix 4 for reference. After all fills were done; Point rainfall data is used to determine the spacial rainfall distribution over the entire basin. Thiessen polygon method, where by the influence of each rainfall station is determined and the weighted average rainfall estimated; is selected for this purpose. The Thiessen polygon generated for the Nebiyou k Page 39
Groundwater potential assessment and characterization of Genale-Dawa River basin
Genale-Dawa River basin on the bases of 23 rain fall stations is presented in Fig10. The area of the Thiessen polygon bounding each station receives the same amount of rainfall as the station.
Figure 10 Thiessen polygon diagram generated on Genale-Dawa Basin
The average annual rainfall observed over these stations is shown in Table 1.
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Table 1 Location and average precipitation of rainfall gauging stations used for areal rainfall calculation
Annual cumulative of No. Met. station Location (UTM) monthly Mean rainfall Station Code Latitude, N Longitude, E (mm) 1 ABISSA (S-21) 70952 403855 747.8054708 2 SOFOUMER (S-20) 65400 405024 585.4909383 3 KEBADO(S-2) 62600 382000 1398.618571 4 FISHA GENET(S-6) 60400 381100 1394.102499 5 YIRGA CHEFE(S-5) 60902 381207 1333.485122 6 FILTU(S-12) 50623 403835 408.485119 7 MEGA(S-11) 40413 381914 615.2595465 8 GENALE DONTA(S-13) 54300 393700 1297.203825 9 DADIME (S-10) 52228 380330 758.3530511 10 FINCH WUHA (S-19) 52335 381611 765.9513511 11 GEDEBE(S-7) 55420 381422 1517.122361 12 BIDER (S-14) 54600 393700 781.2535876 13 WADERA (S-15) 54300 391500 898.3692765 14 ODDO SHAKISO (S-16) 55000 385800 1022.980829 15 TEFERKELA (S-8) 60000 382300 1724.167539 16 BULLE (S-1) 61813 382414 1533.079242 17 ALETA WONDO(S-3) 63614 382505 1582.162685 18 BERRA(S- 4) 64236 382505 1303.217431 19 DELLO MENA (S-17) 62500 395000 1021.439937 20 MELKA ODDA (S-18) 70116 394934 559.8847648 21 AGARFA (S-22) 71600 394900 1086.15793 22 GESERA (S-19) 70800 395600 1007.416663 23 DELLO SIRBO (23) 71500 402800 1023.40185
4.3. Base flow separation
Monthly stream flow data of 12 gauging stations over the basin was collected. The length of data record ranges from 15 to 20 yrs. prior to using these data for the determination of base flow contribution to rivers, data filling and quality check was done using outlier testing as shown below. Data filling was done using single and multiple regression techniques
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alternatively for different stations. The choice of using either one was depending on the hydrological similarity between the gauging stations. On the other hand quality check was done with outlier testing by seating lower and upper limit using the formulation shown below. After all the necessary data check and fills were done, digital filter method was employed to perform base flow separation of different stations (Appindex1).
Where
YL is the log of high or low outlier limit,
Ym is the mean of the log of the sample flows,
Sy is the standard deviation of the logs of the sample flows, and
Kn is the critical deviation
Table 2 Monthly base flow contribution at gauging stations
Station Name Monthly Month yadot welemel wyib togona mormora mesol mana healgo genale awata dimtu deyou Total (M3) jan 4.05 11.73 1.29 0.89 9.19 2.04 0.08 0.65 42.75 8.36 0.02 10.38 2742.50 feb 3.01 9.20 1.60 0.47 6.52 1.55 0.08 0.55 44.14 10.21 0.01 11.15 2654.48 mar 4.38 10.27 2.71 0.33 7.21 1.51 0.10 0.59 57.14 10.38 0.04 11.86 3195.32 apr 5.47 14.51 3.13 0.45 10.33 1.96 0.23 0.80 84.17 11.15 0.05 12.75 4350.28 may 5.89 16.30 2.40 1.08 12.73 2.21 0.27 0.92 138.18 11.86 0.06 14.25 6184.58 jun 6.02 16.56 4.16 1.26 12.71 2.29 0.29 0.82 124.97 12.75 0.09 14.77 5900.45 jul 6.31 18.17 45.49 1.15 13.48 2.49 3.64 9.32 157.15 14.25 0.69 11.74 8516.10 aug 6.63 19.49 6.13 16.31 14.09 30.51 0.45 0.72 206.51 14.77 0.06 6.74 9672.67 sep 6.94 214.22 6.65 1.84 14.76 2.79 0.46 0.65 211.70 11.74 0.07 4.49 14289.19 oct 76.75 22.23 4.00 2.11 162.57 3.00 0.48 0.74 247.42 6.74 0.08 4.17 15909.17 nov 7.87 22.66 2.43 2.29 17.18 3.11 0.34 0.79 826.60 4.49 0.09 8.18 26881.03 dec 6.32 16.85 1.25 1.72 14.33 2.87 0.17 0.69 811.52 4.17 0.04 9.83 26092.85
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4.4. Flow system boundary
Based on the previous discussion on conceptualization of flow system boundary, Dirichlet and Newman boundary conditions have been conceptualized in the model as described below.
Rivers as previously discussed are represented in the model with a set of nodal points that collectively make up the river system. Sections of rivers that cut through an aquifer to a considerable length have been identified in the region, they are considered as gaining rivers and in hydrological terms they are named Perennial Rivers; ones that do not dry throughout the season of the year. Some sections of the rivers that displayed such characteristics include Genale, Dawa, Gestro, Mena Weyeb and others small streams as well. These rivers, since they are gaining and are exposed to atmospheric pressure, can be taken as constant head boundaries (Dirichlet condition). Hence, the set of nodes that represent these rivers are set to have known hydraulic head equivalent to atmospheric head.
On the other hand, river sections that lose water to an aquifer are named losing or intermittent rivers. These types of rivers recharge the ground water with a loss rate that is variable spatially and temporally. They can be taken as specified flow boundary if the loss or gain rate of the stream is known spatially and temporally (Reilly, 2001). However, this recharge rate is difficult to determine explicitly. Nevertheless, considering recharge to an aquifer ultimately results from rain fall; recharge from loosing rivers in this study area is implicitly represented with rate of recharge by precipitation.
Moreover, the peripheral physical boundaries of the discretized region lie mostly on topographic highs locking the river basin, the regional groundwater divides are also assumed to align to these topographic conditions. Hence, it is assumed that the boundaries of the basin are no flow boundaries. In addition, the bottom surface of the region which in this study is taken to be 2500km deep is assumed to have an impermeable bed making it a no flow boundary. At the same time, considering recharge due to precipitation is a major source to groundwater in the basin, this study also takes recharge rate into ground-water as a specified flow boundary condition along the top boundary of the groundwater model.
The figure below shows the constant head river boundaries colored light blue and peripheral no flow boundary of the model with dark bold line
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Flow system Boundaries
Perennial rivers network Model boundary
Figure 11 Flow system Boundaries
4.5. Model calibration
As described in the methodology section model calibration in this study is done using manual trial and error approach. Where, values of hydraulic properties are manually tuned in an attempt to make agreement between simulated and recorded field hydraulic head distribution data. Distribution of horizontal and vertical hydraulic conductivities for different geologic classes on the study area are therefore, adjusted as calibration parameters until a satisfactory agreement is made between the measured and simulated field data. When a best fit is achieved the corresponding hydraulic conductivity distribution is assumed to be representative of the study area in the conceptualized region. After many successive trial and error procedures were done, the best agreement made is evaluated quantitatively using a scatter plot between the two set of data.
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In the scatter plot graph, ideal fit is expected to have a strictly linear relationship with a value of correlation coefficient , R2=1 and a randomly scattered plot is an indication of poor fit with a value of R2=0. The calibration check for the model in this study is shown using a scatter plot.
Graphical evaluation of calibrated results 3500
3000 R² = 0.9997
2500
2000
1500
1000
Simulated static Simulated waterlevel (m) 500
0 0 500 1000 1500 2000 2500 3000 3500 Recorded static water level (m)
Figure 12 Evaluation of calibration results using scatter plot between hs and hm
Additional quantitative evaluation of model calibration result is also done using average indicators and the following results were obtained
5. The mean error (ME)
6. The mean absolute error (MAE)
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7. The root mean squared error (RMS)
Qualitative check of calibration results is also done with contour maps generated using simulated and field recorded hydraulic head data. Resemblance between the two contour maps serves as a qualitative check for the model output. Fig 13 and Fig 14 shows the contour maps generated using field recorded and simulated hydraulic head values.
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UTM Longitude Y
UTM X Latitude
Figure 13 Ground water contour map generated with recorded hydraulic head
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UTM Longitude Y
UTM X Latitude
Figure 14 Groundwater contour map generated with simulated head
For the most part, the contour lines generated based on simulated and recorded data seem to agree well. The resemblance between the two contour maps is an indication of how well the model is calibrated and that the model represents the real system. Even thought complete similarity is unachievable, a faire resemblance seems to exist. Considering the resolution of modeling and quality of data used, this result has been taken acceptable.
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From the calibrated model, the average recharge percentage of rainfall was estimated to be 10% of the total rainfall received by the catchment. Keeping in mind the arid climate of the basin and morphological setting of the basin that causes sloppy areas; the result obtained is taken acceptable. The resulting horizontal and vertical hydraulic conductivity distribution for individual geologic classes are presented in table
Table 3 Hydraulic conductivity values of different geologic medium on Genale-Dawa Basin (Geologic coding is presented in appendix 3 and is consistent with fig 4)
Hydraulic Conductivity Geologic class code Kx(m/s) Ky(m/s) Kz(m/s) Keq Ja 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Jg 1.30E-05 1.30E-05 8.64E-05 1.20E-07 Jh1 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Jh2 1.73E-05 1.73E-05 2.16E-04 2.54E-07 ka 8.64E-05 8.64E-05 8.64E-05 8.03E-07 kg1 8.64E-05 8.64E-06 2.16E-04 4.02E-07 kg2 7.78E-04 7.78E-04 8.56E-04 2.28E-05 km 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Nb 8.64E-04 8.64E-04 8.64E-05 8.03E-06 NMt 8.64E-04 8.64E-05 8.64E-05 2.54E-06 NMv 8.64E-04 8.64E-05 1.73E-04 3.59E-06 Nn 8.64E-04 8.64E-05 1.73E-04 3.59E-06 Ntr2 8.64E-04 8.64E-05 8.64E-05 2.54E-06 Ntr3 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcdt 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcdt1 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcdt2 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcgb1 5.18E-04 5.18E-05 1.30E-04 1.87E-06 Pcgb2 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcgd 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Pcgn1 8.64E-08 8.64E-08 8.64E-08 2.54E-11 Pcgn2 8.64E-08 8.64E-08 4.32E-07 5.68E-11 Pcgn3 8.64E-08 8.64E-08 8.64E-08 2.54E-11 Pcgn4 8.64E-08 8.64E-08 8.64E-08 2.54E-11 Pcgn5 1.73E-07 2.33E-06 1.73E-08 8.35E-11 Pcgn6 8.64E-07 8.64E-09 8.64E-07 8.03E-11 Pcgn7 8.64E-07 8.64E-07 8.64E-08 2.54E-10
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Pcgn8 8.64E-07 7.78E-05 4.32E-04 1.70E-07 Pcgn9 4.32E-06 7.78E-07 8.64E-07 1.70E-09 Pcgn10 8.64E-07 8.64E-07 2.16E-06 1.27E-09 Pcgn11 8.73E-07 1.30E-06 8.73E-07 9.94E-10 Pcgt1 4.75E-08 4.75E-08 8.64E-08 1.40E-11 Pcgt2 4.32E-07 4.32E-07 2.16E-07 2.01E-10 Pckb 8.64E-07 8.64E-08 8.64E-08 8.03E-11 Pcs1 8.64E-08 8.64E-08 8.64E-08 2.54E-11 Pcs2 8.64E-08 8.64E-07 2.16E-07 1.27E-10 Pcs3 8.64E-07 8.64E-07 8.64E-07 8.03E-10 Pcum1 8.64E-08 8.64E-08 8.64E-08 2.54E-11 Pcum2 8.64E-08 4.32E-07 8.64E-08 5.68E-11 Pcum3 8.64E-08 8.64E-07 8.64E-08 8.03E-11 Pcum4 8.64E-08 8.64E-08 8.64E-07 8.03E-11 PNb1 1.73E-04 1.73E-05 1.73E-05 2.27E-07 PNb2 8.64E-05 8.64E-05 8.64E-05 8.03E-07 PNi 8.64E-05 1.30E-04 1.30E-04 1.20E-06 PNtr1 8.64E-08 8.64E-08 8.64E-07 8.03E-11 Q 2.16E-03 2.16E-03 2.16E-03 1.00E-04 Q6 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Qa 4.34E-03 4.34E-03 4.34E-03 2.86E-04 Qe 1.30E-06 1.73E-06 1.73E-05 6.22E-09 Qv 4.34E-04 4.34E-04 4.34E-04 9.03E-06 Qv1 1.73E-04 1.73E-03 2.16E-03 2.54E-05 Qv2 8.64E-05 8.64E-06 8.64E-05 2.54E-07 Qv3 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Qv4 8.64E-05 8.64E-05 2.16E-05 4.02E-07 Qv5 8.64E-05 8.64E-05 8.64E-05 8.03E-07 Tsy 1.30E-04 8.64E-04 1.30E-04 3.81E-06
The estimated hydraulic conductivity parameter values appear to vary between 8.64E-09 to 4.34E-03 with most of the geologic medium falling in range between 8.03E-07and 2.54E-11. This distribution seem to be reasonable when we consider; that the geology of Genale Dawa River Basin is highly diversified containing Karastic Aquifer characteristics near and around Sofoumer, concentrated geological discontinuities observed on the western parts of the basin and low hydraulic conductivity metamorphic rocks in the northern and central part of the basin.
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The geologic map on Fig 4 of the basin shows the configuration of individual geology classes that are listed in the table above.
After the model calibration is complete, groundwater table (pizometric head) of the region is calculated for each month in a year; using the calibrated model at nodal points. Afterwards conditions of possible maximum and possible minimum porosities of individual geologic mediums are taken. For each of these two conditions, monthly storage calculations in individual finite elements are done. The average of these tow conditions is used to determine groundwater table configuration in each month. This monthly groundwater table location showed seasonal fluctuation of the water table as shown in Fig 16. Thereafter, the determination of replenishable groundwater potential of the region is done by quantifying the amount of water that is only temporarily stored in the ground and drains to rivers, springs and lost due to evapotranspiration at latter times. Therefore minimum water table in the seasonal fluctuation is taken as benchmark for zero replenishable ground water storage.
1.285E+10 1.28E+10 1.275E+10 1.27E+10
1.265E+10 1.26E+10 1.255E+10 Volume(m^3) 1.25E+10 1.245E+10 1.24E+10 1.235E+10 0 2 4 6 8 10 12 14 Month
Figure 15 mean monthly water table fluctuation
The Ground water fluctuation trend seems to be comparable with rainfall series of the region for the most part, but near the 9th month it showed erratic behaviour. This could be a Nebiyou k Page 51
Groundwater potential assessment and characterization of Genale-Dawa River basin cumulative result of a very complex geologic composition of the region and spatially non uniform rainfall pattern in the region.
In order to determine the total replenishable ground water reserve, monthly contribution of groundwater to rivers shall also be taken in to account. Accordingly base flow separation of stream flow data was done using single parameter digital filter approach (Appendix 1) and the result was added to the volume of replenishable ground water obtained from the model. This has resulted in 2.78BM3 as total replenishable groundwater in Genale Dawa basin; the result is shown in table below.
Table 4 Total Replenishable Ground Water Calculation
Replenishable Base flow Total monthly Ground Water contribution to Replenishable Month (M3) rivers(M3) Ground Water (M3) jan 1.05E+08 2.74E+03 1.05E+08 Feb 3.03E+08 2.65E+03 3.03E+08 mar 3.79E+08 3.20E+03 3.79E+08 apr 4.11E+08 4.35E+03 4.11E+08 may 2.65E+08 6.18E+03 2.65E+08 jun 1.02E+08 5.90E+03 1.02E+08 jul 0.00E+00 8.52E+03 8.52E+03 aug 2.63E+08 9.67E+03 2.63E+08 sep 1.63E+08 1.43E+04 1.63E+08 oct 2.54E+08 1.59E+04 2.54E+08 nov 2.65E+08 2.69E+04 2.65E+08 dec 2.64E+08 2.61E+04 2.64E+08 Total Replenishable Ground Water 2.78E+09
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Hydrogeology of Genale Dawa Basin
From the hydraulic conductivity values obtained from calibration it can be seen that many geological formations have similar hydraulic properties, for instance geologic mediums (1,3,5,8,14,15,16,17,43,47,53,54,55 and 11,12) have the same hydraulic conductivities where as others if not the same they have close similarity. Geologic characteristics of the Genale-Dawa River Basin can therefore be better understood if geologic mediums with similar hydraulic conductivity are grouped together. Accordingly, the following groups have been made
High hydraulic conductivity geologic medium (>1×10*-4 m/s) Moderately high hydraulic conductivity geologic medium (1×10*-4 to 1×10*-5 m/s) Moderately low hydraulic conductivity geologic medium (1×10*-5-1×10*-7 m/s) Low hydraulic conductivity geologic medium (1×10*-7 to 1×10*-12 m/s)
Figure 16 Hydro geologic map of Genale Dawa basin
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It is also possible to plot SWL vs. Ground elevation in order to inspect how water table behaves in relation to elevation changes of the ground (terrain) see Fig 18.
3500
3000
2500
2000
SWL (m)SWL 1500
1000
500
0 0 500 1000 1500 2000 2500 3000 3500 Ground Elivation (m)
Figure 17 Relationship between elevation of ground surface and water table
It can be seen on the plot that surface terrain and ground water profile are replica of each other. This shows that flow velocity of ground water is parallel ground surface and Ground water system in the region tends to be dominated by an unconfined aquifer system. (Fetter, 2001)
Additionally by looking at the velocity field distribution it is possible to identify major recharging and discharging areas. Circles in read show areas of recharge with dispersing velocity vectors, where as circles in green show areas of discharge with collecting velocity vectors. But it should be noted that identified recharging sites can also be acting as discharging areas at the same time and vice versa. This can be witnessed if we look at the recharging areas at the northern
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Groundwater potential assessment and characterization of Genale-Dawa River basin part of the basin; all the velocity vectors are in dispersing position showing a major recharge taking place but at the same time perennial rivers emanate from those areas showing that it is also acting as a discharging site. Moreover it is seen that discharging sites do coincide with river networks showing points at which ground water contributes to rivers. But at points where recharging site is away from rivers, it is possibly an indication that the aquifer is discharging to an underlying strata.
Major recharging area Major discharging area Perennial River network
Figure 18 Identification of Recharging and Discharging areas In Genale Dawa Basin
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5. Conclusion and Recommendation
5.1. Conclusion
Groundwater modeling has versatile applications in groundwater resources management that can be used for groundwater pollution management and more. This study used finite element based three dimensional groundwater modeling program called TAGSAC to estimate the groundwater recharge potential as described in the previous chapters. The findings of this research showed that the basin has an average recharging potential of 2.78 B CM. This is in order with previous estimations made by WAPCOS in 1990 which was 0.433BCM using recharge area approach. But considering that recent data have also been incorporated in this study and that a different methodology has been employed, the result can be taken as a good estimation. An improved delineation has also been used that helps in making better estimate. However, further study on estimation of extraction factor shall be done. This factor accounts for the sociological, botaniuque and other concerned factors to determine the safest amount of groundwater that can be extracted from the basin. In addition, this study also attempted to characterize the groundwater flow system of the basin by preparing hydro-geological map and contour map, which can be useful in selection of well field in the future.
It should also be noted that this study has faced some challenges due to limitations in computational resources. This has restricted the model not to use finer finite element sizes which would have a positive effect towards better ground water potential estimate. The luck in availability of detailed geological data has also limited the model with a single layer that represents the aquifer system in the vertical extent. With detailed geological investigation an improved model with multiple layers can be developed. Therefore, future studies on the basin can improve on these to get a better estimation.
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5.2. Recommendation
Based on the results obtained from the study the following recommendations are forwarded;
The identified sites of recharge area shall be protected from polluting chemicals to assure quality of the regions groundwater. Forestation of this area can also increase the available groundwater reserve by increasing the amount of infiltration and percolation hence; it is recommended to plane and execute environmental protection projects in these regions. Identified discharging areas can be used as well fields after carrying out the necessary detail investigation on the site. Therefore the regional authorities can consider ground water based water supply schemes for domestic and industrial purposes.
Regular well monitoring shall be planned and executed in the region to gather better quality data and widen our understanding of the basins groundwater flow system which can be used as an input for future study. Concerned authorities shall also provide a framework that can enforce the collection and report of well inventory data including geological log, time log and other standard records at the time of well drillings and completion.
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Appendix 1 Continuous Base flow Separation Method
Digital filter is a frequency analysis method initially developed for signal analysis. Now days it is widely applicable for base flow separation in Hydrology
Approach:
Use a numerical algorithm (a digital filter) to partition the streamflow hydrograph into “high frequency” (direct runoff) and “low frequency” (baseflow) components. One type of digital filter approach is show as follows. (Nathan and McMahon, 1990)
Terms:
Qk streamflow at time step k
Rk direct runoff at time step k
Bk baseflow at time step k
Parameter:
α base flow filter parameter, it’s an attenuation coefficient between 0.9 and 0.995. The bigger the attenuation the stronger the runoff, and the lesser the base flow a value of 0.95 was used for this study.
Algorithm:
At each time step:
Check:
If <0 then =0
If >then =
Compute base Flow:
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= - Appendix 2 Water point calibration data
No. TYP SITE_NAME REGION ZONE WEREDA UTM_X UTM_Y SWL 1 BHE Ali Town No 1 OROMIY BALE Agarfa 60306 80034 10 2 BH Ginir OROMIYA BALE Ginir 690001 788000 12 3 BH Melka Oda OROMIYA BALE Ginir 695160 774040 8.25 4 BH Melka Oda OROMIYA BALE Ginir 703394 775705 22 5 BH Melka Oda No. 1 OROMIYA BALE Ginir 705148 773461 12 6 BH Tullicha No 3 OROMIYA BALE Ginir 700223 779159 21 7 BH Melka Buta OROMIYA BALE Goro 678614 766611 8 8 BH Sinana No 3 State Farm OROMIYA BALE Goro 643014 778423 69 9 BH Abasirba No 1 OROMIYA BALE Meda 582080 650554 5 10 BH Abasirba No 2 OROMIYA BALE MedaWelabu 581677 650904 16 11 BH Bidire BH5 OROMIYA BALE MedaWelabu 572127 653664 15 12 BH Bidire No 1 OROMIYA BALE MedaWelabu 572526 654482 8 13 BH Bidire town No 2 OROMIYA BALE MedaWelabu 571808 654324 8 14 BH Bidire town No 3 OROMIYA BALE MedaWelabu 570758 654018 13 15 BH Elabidire No 1 OROMIYA BALE MedaWelabu 574487 652798 8 16 BH Elabidire No 2 OROMIYA BALE MedaWelabu 576093 652238 17 17 BH Meda No 1 OROMIYA BALE MedaWelabu 598534 643567 6 18 BH Meda No 2 OROMIYA BALE MedaWelabu 598280 643759 9 19 BH Oborso No 2 OROMIYA BALE MedaWelabu 544462 678342 21 20 BH Oda OROMIYA BALE MedaWelabu 551275 672097 8 21 BH Burkitu Derara No 1 OROMIYA BALE MenaWelabu 593844 712069 13 22 BH Chiri Harewa No 1 OROMIYA BALE MenaAngetu 586196 708139 18 23 BH Chiri Harewa No 2 OROMIYA BALE MenaAngetu 586614 708310 12 24 BH Dayu Harewa WELL NO 3 OROMIYA BALE MenaAngetu 607513 717505 16 25 BH Erba No 1 OROMIYA BALE MenaAngetu 596075 713399 12 26 BH Erba No 2 OROMIYA BALE MenaAngetu 596548 713704 15.3 6 27 BH Gomgoma No 1 OROMIYA BALE MenaAngetu 592604 698166 8 28 BH Mena town BH4 OROMIYA BALE MenaAngetu 594921 708624 6 29 BH Weltai Gudina No 1 OROMIYA BALE MenaAngetu 601334 715599 14 30 BH Weltai Gudina No 2 OROMIYA BALE MenaAngetu 601869 716190 13 31 BH Chelchel No 1 OROMIYA BALE RayituAngetu 729186 763581 17 32 BH Chelchel No 2 OROMIYA BALE Rayitu 729098 763789 15 33 BH Bale Robe Airport OROMIYA BALE Sinanana 614639 786983 11.2 5 34 BH Robe Catholic School OROMIYA BALE SinananaDinsho 610825 784972 5 A Dinsho 4 7 Nebiyou k Page 62
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35 BH Robe No 1 OROMIY BALE Sinanana 61104 78492 5 36 BH Robe No 2 OROMIYA BALE SinananaDinsho 611168 785574 6 37 BH Robe No 4 OROMIYA BALE SinananaDinsho 611132 786809 9 38 BH Robe No 5 (Kindergarten) OROMIYA BALE SinananaDinsho 611259 787344 12 39 BH Robe Town No 6 OROMIYA BALE SinananaDinsho 611734 786996 9 40 BH 25 Km South-east of well No. 12 OROMIYA BOREN AreroDinsho 442690 517633 29.5 41 DW 25/NCA Kms south-east Das/village OROMIYA BORENA Arero 492607 456824 10 42 DW 27 Kms north of Arero local OROMIYA BORENA Arero 488571 548064 1 43 DW 4.5nomads Kms north-west of Arero/ OROMIYA BORENA Arero 470202 527017 2 44 BH Borvillage-Bor WELL NO OROMIYA BORENA Arero 493719 457432 11 45 BH WACHILE /ETH005 OROMIYA BORENA Arero 507301 502398 14 46 DW Wachile HDW OROMIYA BORENA Arero 511092 501036 6.1 47 BH Wachile No. 1 OROMIYA BORENA Arero 506623 502415 21 48 BH Wachile No. 3 OROMIYA BORENA Arero 512015 511166 20 49 BH Fincha's (Jiges) OROMIYA BORENA Bule 423366 586767 3.5 50 BH Kilenso No. 1 OROMIYA BORENA BuleHora 422450 606517 15.9 2 51 DW 10 Kms Mega-Yabelo OROMIYA BORENA DireHora 418609 458701 15 52 DW 12 Kms north-west of Mega OROMIYA BORENA Dire 413677 457173 6 53 DW 12.5 Kms south-west of Mega/ OROMIYA BORENA Dire 418413 442543 13 54 DW 16Nomads Kms west of Nomads OROMIYA BORENA Dire 454376 465377 15 55 DW 2 Kms south of Mega/Nomads OROMIYA BORENA Dire 425885 443701 5 56 DW 40 Kms north-west of Mega / OROMIYA BORENA Dire 392725 474231 2.5 57 BH DUBLUKNomads ETH/025 OROMIYA BORENA Dire 420127 482938 19 58 BH Dublick No. 1 OROMIYA BORENA Dire 421713 481425 22.6 5 59 BH Dublick No. 5 OROMIA Y BORENA Dire 420481 483571 19.4 60 DW Dubluk /Ato Awaticha OROMIYA BORENA Dire 420470 481732 19 61 DW EAGDER DW ETH/007 OROMIYA BORENA Dire 485559 433260 24 62 BH EGDER ETH/046 OROMIYA BORENA Dire 485168 433054 24.5 63 BH HIDILOLA ETH/036 OROMIYA BORA EN Dire 453280 408131 5 64 BH MEGA ETH/010 OROMIYA BORENA Dire 424005 448722 16 65 BH Mega No. 1 OROMIYA BORENA Dire 422146 448561 16.8 66 BH QA GOFA ETH/042 OROMIYA BORENA Dire 433574 471008 24 67 BH Site-147 No. 1 OROMIYA BORENA Dire 467612 429500 17 68 BH TUKA ETH/013 OROMIYA BORENA Moyale 485098 398795 6 69 BH 12 Km South-west of well No. OROMIYA BORENA Yabelo(OR) 425964 532009 33.2 70 DW 3.511 Ministry Kms to Teltele of agriculture-Yabello road OROMIYA BORENA Yabelo 396519 539796 5 71 DW 40/ village Kms north -west of OROMIYA BORENA Yabelo 455771 552100 9.2 72 BH KedaleArero/local No. 1nomads OROMIYA BORENA Yabelo 415925 545134 12 73 BH Oda No. 1 OROMIYA BORENA Yabelo 428743 583819 5 74 BH Surupa WELL NO 2 OROMIYA BORENA Yabelo 428583 577823 2 75 BH Kibre Mengist No. 1 OROMIYA GUJIA Adola 498773 649786 7.72 76 BH Kibre Mengist high school No. 2 OROMIYA GUJI Adola 498150 649788 24.5 A 3 8 Nebiyou k Page 63
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77 BH Kibre Mengist town BH9 OROMIY GUJI Adola 49884 65062 21 78 BH Chemeri Bachano No. 1 OROMIYA GUJI Kercha 421867 625880 53.7 5 79 DW Adadi OROMIYA GUJI Liben 545577 586877 3.3 80 DW Bokola OROMIYA GUJI Liben 537227 602860 3.1 81 DW Buledi /Haro/ OROMIYA GUJI Liben 591350 591358 2.8 82 DW Buradera OROMIYA GUJI Liben 559171 579231 5 83 DW Buradhera OA ROMIY GUJI Liben 561256 576286 9.38 84 BH Debeno OROMIYA GUJI Liben 574203 589736 55 85 DW Dolcha OROMIYA GUJI Liben 535364 626697 5.4 86 DW Gobicha OROMIYA GUJI Liben 562895 589176 4 87 DW Hardot OROMIYA GUJI Liben 548712 586178 5 88 DW Haro /Kobadi/ OROMIYA GUJI Liben 564291 591332 1.21 89 DW Harokelo DW6 OROMIYA GUJI Liben 543297 613712 20 90 DW Kerero OROMIYA GUJI Liben 546855 593034 8.9 91 DW Kersemele OROMIYA GUJI Liben 551936 591979 4.5 92 DW Mede OROMIYA GUJI Liben 544174 597520 5.1 93 DW Melkaguba OROMIYA GUJI Liben 533753 538871 5 94 DW Mersa OROMIYA GUJI Liben 563918 584858 6 95 DW Mucho OROMIYA GUJI Liben 541165 619867 6.62 96 DW Negele (Kela) DW2 OROMIYA GUJI Liben 562737 590073 2.5 97 BH Negele AP No. 2 OROMIYA GUJI Liben 578029 585359 59.3 98 BH Negele Army base No. 6 OROMIYA GUJI Liben 562787 591326 2.5 99 BH Negele Borena OROMIYA GUJI Liben 570175 583968 4 100 BH Negele No. 6 OROMIYA GUJI Liben 562949 588106 4 101 DW Negele Water scheme OROMIYA GUJI Liben 563392 588294 1.88 102 DW Nura humba OROMIYA GUJI Liben 545820 604142 2.8 103 BH Siminto OROMIYA GUJI Liben 570019 576570 15.2 5 104 DW T. Dhelan OROMIYA GUJI Liben 548873 581214 5.28 105 DW Wofe OROMIYA GUJI Liben 566221 590530 4.1 106 BH Awata No. 1 OROMIYA GUJI Odo 490627 639653 8.18 107 BH Arbegona SNNPA SIDAMA ArbegonShakiso 468910 739756 6 108 BH Bensa Daye SNNP SIDAMA Bensaa 482002 720000 13.5 109 BH Hagere Selam BH16 SNNP SIDAMA Hula 446570 716710 100 110 DW Chereti SOMALI AFDER Chereti 825007 600005 1.2 111 DW Bur amino SOMALI AFDER Dolo Bay 827150 476660 4.3 112 DW Biyoole SOMALI LIBEN Dolo Odo 790995 468129 2.5 113 DW Dipi (near Dolo) DW4 SOMALI LIBEN Dolo Odo 821884 466882 5 114 DW Dytuli SOMALI LIBEN Dolo Odo 789919 444469 15.2 115 DW Geled SOMALI LIBEN Dolo Odo 785697 444037 5 116 DW Golome SOMALI LIBEN Dolo Odo 828659 475822 7.5 117 DW Kole SOMALI LIBEN Dolo Odo 812036 490544 4.6 118 DW Niman SOMALI LIBEN Dolo Odo 786693 439740 4 8 2 Nebiyou k Page 64
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119 DW Shambel SOMALI LIBEN Dolo Odo 81681 47502 7.2 120 DW Mesajid-1 SOMALI LIBEN Filtu 696517 569379 20 121 DW Mesajid-2 SOMALI LIBEN Filtu 696492 569392 17.5 122 DW Rideb SOMALI LIBEN Filtu 745707 579876 4.5 123 DW Sirba SOMALI LIBEN Filtu 612300 539820 2.86 124 BH Chilango No. 1 SOMALI LIBEN Moyale 613309 461252 21 125 BH Dawa South 1 SOMALI LIBEN Moyale(SM) 600002 475007 21 126 BH El Der No. 1 SOMALI LIBEN Moyale(SM) 584190 427690 24.4 127 BH El Gof SOMALI LIBEN Moyale(SM) 506216 425885 19.5 128 BH El Gof SOMALI LIBEN Moyale(SM) 506752 426509 20.7 129 BH El Kalu SOMALI LIBEN Moyale(SM) 520776 416942 19.3 130 BH El Kalu No. 1 SOMALI LIBEN Moyale(SM) 520359 416300 19 131 BH El Leh SOMALI LIBEN Moyale(SM) 520547 416540 20 132 SP Elabidu Spring OROMIY BALE Agarfa(SM) 601542 796645 0 133 SP Kasowara OROMIYA BALE Agarfa 597014 796120 0 134 SP Burkitu (Cheketa Urene) OROMIYA BALE Berbere 643002 753078 0 135 SP Haro Dumal OROMIYA BALE Berbere 629580 747194 0 136 SP Amigna Shirar Spring No 1 OROMIYA BALE Gasera 637198 811047 0 137 SP Amigna Shirar Spring No 2 OROMIYA BALE Gasera 637317 810938 0 138 SP Amigna Shirar Spring No 3 OROMIYA BALE Gasera 637349 810934 0 139 SP Ashute Gaguro No 1 OROMIYA BALE Ginir 689135 784078 0 140 SP Ashute Gaguro No 2 OROMIYA BALE Ginir 689104 784074 0 141 SP Ashute Gaguro No 3 OROMIYA BALE Ginir 689077 784075 0 142 SP Chancho Ardaterie OROMIYA BALE Ginir 691465 783644 0 143 SP Doyo OROMIYA BALE Ginir 683499 785713 0 144 SP Elani Abiyu OROMIYA BALE Ginir 682446 781203 0 145 SP Ginir (9km NE) SP1 OROMIYA BALE Ginir 697504 797406 0 146 SP Keteti No 1 OROMIYA BALE Ginir 699650 789930 0 147 SP Keteti No 2 OROMIYA BALE Ginir 699586 789964 0 148 SP Oda Roba OROMIYA BALE Ginir 695100 788854 0 149 SP Elasa Iteya OROMIYA BALE Goba 618696 772458 0 150 SP Misira Spring OA ROMIY BALE Goba 619557 775384 0 151 SP Addis Alemana Water Supply OROMIYA BALE Goro 650470 776862 0 152 SP AwugiegeshScheme Spring OROMIYA BALE Goro 644266 770335 0 153 SP Burkitu No 1 OROMIYA BALE Goro 666321 770997 0 154 SP Burkitu No 2 OROMIYA BALE Goro 666322 770971 0 155 SP Dodimol OROMIYA BALE Goro 657505 770934 0 156 SP Goro (Dadimos) OROMIYA BALE Goro 665706 772106 0 157 SP Weltai Chefa OROMIYA BALE Goro 664704 771780 0 158 SP Chali Spring No 1 OROMIYA BALE Kokosa 482308 744552 0 159 SP Chali Spring No 2 OROMIYA BALE Kokosa 482313 744565 0 160 SP Churisa No 2 OROMIYA BALE Kokosa 473800 756156 0 A 1 8 Nebiyou k Page 65
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161 SP Churisa Spring No 1 OROMIY BALE Kokosa 47420 75501 0 162 SP Diki Liemeni Spring OROMIYA BALE Kokosa 481399 743995 0 163 SP Ebicha Spring OROMIYA BALE Kokosa 486220 749951 0 164 SP Halila Spring OROMIYA BALE Kokosa 477555 745152 0 165 SP Rera OROMIYA BALE Mena 542380 722117 0 166 SP Wabero Spring OROMIYA BALE MenaAngetu 589631 710241 0 167 SP Antokia Spring OROMIYA BALE NenseAngetubo 512179 727219 0 168 SP Bochesa OROMIYA BALE Nensebo 513276 716427 0 169 SP Bulga OROMIYA BALE Nensebo 513747 727709 0 170 SP Burka Mena Beromsa OROMIYA BALE Nensebo 506228 743300 0 171 SP Burkitu Jeldo Spring OROMIYA BALE Nensebo 506814 743335 0 172 SP Burkitu Spring OROMIYA BALE Nensebo 512136 728839 0 173 SP Giorgis Spring No 1 OROMIYA BALE Nensebo 512331 727759 0 174 SP Giorgis Spring No 2 OROMIYA BALE Nensebo 512351 727831 0 175 SP Huro OROMIYA BALE Nensebo 510446 733077 0 176 SP Ketena No 1 Spring OROMIYA BALE Nensebo 512217 727368 0 177 SP Ketena No 3 Spring OROMIYA BALE Nensebo 511670 728888 0 178 SP Korema OROMIYA BALE Nensebo 512246 724994 0 179 SP Koro Doyo Spring OROMIYA BALE Nensebo 506482 751455 0 180 SP Mewa OROMIYA BALE Nensebo 512679 732180 0 181 SP Solena OROMIYA BALE Nensebo 513637 730981 0 182 SP Werka Health Center OROMIYA BALE Nensebo 511917 728621 0 183 SP Werka Town (Tebel Spring No 1) OROMIYA BALE Nensebo 512514 729265 0 184 SP Werka Town (Tebel Spring No 2) OROMIYA BALE Nensebo 512507 729242 0 185 SP Abakarazalo Spring No 1 OROMIYA BALE Sinanana 592957 787845 0 186 SP Abakarazalo Spring No 2 OROMIYA BALE SinananaDinsho 592942 787852 0 187 SP Chelo Robe Meliyu OROMIYA BALE SinananaDinsho 601201 783643 0 188 SP Robe Oda Robe Meliyu OROMIYA BALE SinananaDinsho 599554 785875 0 189 SP Werabo Robe Meliyu OROMIYA BALE SinananaDinsho 599695 785278 0 190 SP 1.5 Kms north-east of Arero / OROMIYA BOREN AreroDinsho 481851 525217 0 191 SP 2Metagefersa Kms south- west of Dekole OROMIYA BORENA Dire 377928 470726 0 192 SP 40spring Kms (1) north / Dokole-west (2)of Mega OROMIYA BORENA Dire 389244 468586 0 193 SP AboutDolola 20(1) Kms south-west of OROMIYA BORENA Dire 410756 442381 0 194 SP YabeloMega /- TeltelSake e Areri OROMIYA BORENA Teltele 349378 538948 0 195 SP 15 Kms West of Yabelo / Gnaro OROMIYA BORENA Yabelo 389738 541649 0 196 SP 20Village Kms South of Yabelo Deritu OROMIYA BORENA Yabelo 400417 524292 0 197 SP Aboutspring 60 Kms west of Yabelo / OROMIYA BORENA Yabelo 448363 549154 0 198 SP BambuaSankura Wuha SP4 OROMIYA GUJIA Bore 474964 671934 0 199 SP Bensa (Tsebel) OROMIYA GUJI Bore 472331 670517 0 200 SP Benti SP3 OROMIYA GUJI Wadera 533775 638329 0 201 SP Banti Bodo SNNPA GEDEO Kochere 424252 657255 0 202 SP Bedessa SNNP GEDEO Kochere 418577 658939 0 6 5 Nebiyou k Page 66
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203 SP Bonsho SNNP GEDEO Kochere 41829 64936 0 204 SP Bule SNNP GEDEO Kochere 418770 659396 0 205 SP Bule SNNP GEDEO Kochere 418130 660049 0 206 SP Bule SNNP GEDEO Kochere 418227 659672 0 207 SP Chelbsa SNNP GEDEO Kochere 424388 658945 0 208 SP Sakarro SNNP GEDEO Kochere 424573 657059 0 209 SP Sakarro SNNP GEDEO Kochere 417979 658565 0 210 SP Shayssa SNNP GEDEO Kochere 417694 661933 0 211 SP Shayssa SNNP GEDEO Kochere 417939 662325 0 212 SP 01 kebele SNNP SIDAMA Arbegon 467738 722154 0 213 SP 01 kebele SNNP SIDAMA Arbegona 468435 721498 0 214 SP 01 kebele SNNP SIDAMA Arbegona 467722 722263 0 215 SP 01 kebele SNNP SIDAMA Arbegona 467770 721765 0 216 SP Aalawa SNNP SIDAMA Arbegona 470279 726267 0 217 SP Ajerssa SNNP SIDAMA Arbegona 471065 730946 0 218 SP Awokiro SNNP SIDAMA Arbegona 470994 727085 0 219 SP Babo SNNP SIDAMA Arbegona 470167 725485 0 220 SP Bakito SNNP SIDAMA Arbegona 470525 731685 0 221 SP Beto eemcy comp.health. SNNP SIDAMA Arbegona 470188 735403 0 222 SP Bobilicho SNNP SIDAMA Arbegona 470428 725158 0 223 SP Borata SNNP SIDAMA Arbegona 470840 726230 0 224 SP Bukie SNNP SIDAMA Arbegona 470908 723023 0 225 SP Burchano SNNP SIDAMA Arbegona 468536 737955 0 226 SP Butura sine SNNP SIDAMA Arbegona 466566 740299 0 227 SP Chancho SNNP SIDAMA Arbegona 471787 722788 0 228 SP Deguba SNNP SIDAMA Arbegona 472180 723538 0 229 SP Demka SNNP SIDAMA Arbegona 466709 725256 0 230 SP Dentano SNNP SIDAMA Arbegona 472812 725919 0 231 SP Derasha SNNP SIDAMA Arbegona 468666 743494 0 232 SP Dillagenet SNNP SIDAMA Arbegona 470701 726394 0 233 SP Diranto SNNP SIDAMA Arbegona 464222 724813 0 234 SP Diranto SNNP SIDAMA Arbegona 464166 724534 0 235 SP Dobancho SNNP SIDAMA Arbegona 471322 728162 0 236 SP EECMY comp. SNNP SIDAMA Arbegona 471732 730558 0 237 SP Gassie SNNP SIDAMA Arbegona 464146 739781 0 238 SP Gedana 01 kebele SNNP SIDAMA Arbegona 466882 723244 0 239 SP Gerha SNNP SIDAMA Arbegona 464752 741110 0 240 SP Giranto SNNP SIDAMA Arbegona 464308 725225 0 241 SP Gobacho SNNP SIDAMA Arbegona 472863 725033 0 242 SP Golana river SNNP SIDAMA Arbegona 470805 720372 0 243 SP Golga SNNP SIDAMA Arbegona 470230 732484 0 244 SP Golga SNNP SIDAMA Arbegona 470079 732461 0 a 3 6 Nebiyou k Page 67
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245 SP Goto SNNP SIDAMA Arbegon 46885 73751 0 246 SP Gubacho SNNP SIDAMA Arbegona 465643 727144 0 247 SP Gubacho SNNP SIDAMA Arbegona 465844 727241 0 248 SP Guderanto 01 SNNP SIDAMA Arbegona 463974 726309 0 249 SP Hadero SNNP SIDAMA Arbegona 465054 724890 0 250 SP Harake SNNP SIDAMA Arbegona 471239 723710 0 251 SP Health Kella SNNP SIDAMA Arbegona 463685 739481 0 252 SP Heyo SNNP SIDAMA Arbegona 470495 735390 0 253 SP Heyo SNNP SIDAMA Arbegona 466634 739480 0 254 SP Hodamo SNNP SIDAMA Arbegona 470980 73237 0 255 SP Honcho SNNP SIDAMA Arbegona 471302 733928 0 256 SP Honcho SNNP SIDAMA Arbegona 471949 734113 0 257 SP Loke SNNP SIDAMA Arbegona 469850 736179 0 258 SP Malako SNNP SIDAMA Arbegona 471959 729493 0 259 SP Mansuro SNNP SIDAMA Arbegona 466773 725367 0 260 SP Mayka SNNP SIDAMA Arbegona 465285 740872 0 261 SP Melga SNNP SIDAMA Arbegona 464837 727176 0 262 SP Nameto SNNP SIDAMA Arbegona 468638 742495 0 263 SP Primary school SNNP SIDAMA Arbegona 463674 739135 0 264 SP Shasho SNNP SIDAMA Arbegona 468122 743267 0 265 SP Shedama SNNP SIDAMA Arbegona 469623 735437 0 266 SP Soyamo SNNP SIDAMA Arbegona 466105 726081 0 267 SP Terchicha SNNP SIDAMA Arbegona 473057 729041 0 268 SP Tulasene SNNP SIDAMA Arbegona 465727 727544 0 269 SP Urago SNNP SIDAMA Arbegona 468363 735944 0 270 SP Welaku SNNP SIDAMA Arbegona 467759 739174 0 271 SP Worancha SNNP SIDAMA Arbegona 470515 720910 0 272 SP Worancha SNNP SIDAMA Arbegona 471440 719758 0 273 SP Worancha pri. sch. SNNP SIDAMA Arbegona 471310 720011 0 274 SP Wotito SNNP SIDAMA Arbegona 463821 726268 0 275 SP Yeye kebe. 01 SNNP SIDAMA Arbegona 468744 740052 0 276 SP Yeye kebe. 01 SNNP SIDAMA Arbegona 468148 738835 0 277 SP Yeye kebe. 01 SNNP SIDAMA Arbegona 468884 739005 0 278 SP Amello SNNP SIDAMA Arorea sa 491485 699047 0 279 SP Cheffa SNNP SIDAMA Aroresa 489551 707201 0 280 SP Chiro SNNP SIDAMA Aroresa 491005 703602 0 281 SP Dikogora SNNP SIDAMA Aroresa 490597 704029 0 282 SP Fechena SNNP SIDAMA Aroresa 491563 703163 0 283 SP Fenchena SNNP SIDAMA Aroresa 490795 703136 0 284 SP Gerbicho SNNP SIDAMA Aroresa 494324 699748 0 285 SP Merkata SNNP SIDAMA Aroresa 489450 707239 0 286 SP Muremura SNNP SIDAMA Aroresa 493274 689661 0 7 0 Nebiyou k Page 68
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287 SP Muyedo SNNP SIDAMA Aroresa 49167 69959 0 288 SP Nemero SNNP SIDAMA Aroresa 495302 686667 0 289 SP Sericho SNNP SIDAMA Aroresa 494790 702091 0 290 SP Suduwo SNNP SIDAMA Aroresa 489832 706958 0 291 SP Tulanto SNNP SIDAMA Aroresa 490332 703218 0 292 SP Wachota SNNP SIDAMA Aroresa 494112 700647 0 293 SP Wajito SNNP SIDAMA Aroresa 494128 699822 0 294 SP Ware SNNP SIDAMA Aroresa 494068 699845 0 295 SP Weyera SNNP SIDAMA Aroresa 494038 703948 0 296 SP Bello SNNP SIDAMA Bensa 510470 728673 0 297 SP Bensha SNNP SIDAMA Bensa 479474 722424 0 298 SP Burka SNNP SIDAMA Bensa 482265 720024 0 299 SP Chabie SNNP SIDAMA Bensa 484393 710034 0 300 SP Damilie SNNP SIDAMA Bensa 484213 718560 0 301 SP Gado SNNP SIDAMA Bensa 472798 722743 0 302 SP Gidibu SNNP SIDAMA Bensa 482112 714271 0 303 SP Godicho SNNP SIDAMA Bensa 484527 726488 0 304 SP Gomora SNNP SIDAMA Bensa 479065 721767 0 305 SP Gormora SNNP SIDAMA Bensa 479586 721438 0 306 SP Haqansa SNNP SIDAMA Bensa 482219 716066 0 307 SP Harepha SNNP SIDAMA Bensa 475053 717659 0 308 SP Heacho SNNP SIDAMA Bensa 483969 718646 0 309 SP Hodamo SNNP SIDAMA Bensa 480164 722650 0 310 SP Holo SNNP SIDAMA Bensa 484162 719064 0 311 SP Horawa SNNP SIDAMA Bensa 479819 723001 0 312 SP Kore SNNP SIDAMA Bensa 511125 726346 0 313 SP Kurmnie SNNP SIDAMA Bensa 482136 719933 0 314 SP Mirado SNNP SIDAMA Bensa 484037 720020 0 315 SP Motorie SNNP SIDAMA Bensa 483538 725466 0 316 SP Saga SNNP SIDAMA Bensa 481128 714834 0 317 SP Sasinga SNNP SIDAMA Bensa 484209 718544 0 318 SP Wania SNNP SIDAMA Bensa 483681 726096 0 319 SP Kebelanka SNNP SIDAMA Hula 463145 723942 0 320 SP Oudo SNNP GEDEO Kochere 416838 657469 0 321 SP Oudo SNNP GEDEO Kochere 417443 656470 0 322 SP Oudo sukaro SNNP GEDEO Kochere 417239 657281 0 SWL- Static water level 4 3
BH- Bore hole
Sp- Spring
Dw- Dug well
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Appendix 3 Geological coding
(Adopted form integrated water resource development master plan study: Geology sector)
No. Code Description of Geologic class
Quaternary Volcanics and Sediments 1 Q Undivided alluvium, eluvium and lacustrine sediments 2 Qa Alluial deposits: gravel,sand silt and clay 3 Qe Eluvium: Red to reddish brown sandy soil black cotton soil calcrite, minor ferricrite, silt clay 4 Qv6 Augite-olivine-phyric basalt, scoraceous-vesicular with xenoliths of mantle nodules 5 Qv5 pyroclastic surge deposits: Lapilli tuff 6 Qv4 Olivine-phyric-basalt 7 Qv3 pyrocalistic surge deposits: lapilli tuff 8 Qv2 augite-abradorite-lapilli tuff 9 Qv1 pyrcasitic surge deposits: mainly bedded fall deposits scoria, rock fragments 10 Qv scoraceous-vesicular-oilivine-phyric basalt
Tertiary Volcanic Sucessions 11 Nn Nazeret Group: stratoid silics-ignimbites, tuffs, ash, rhyolites, trachye, minor basalt 12 Ntr3 Alkal trachyte flows 13 Ntr2 Alkali trachyte and basalt flows 14 Nb Bulal Basalt flows 15 NMv Upper basal flows 16 PNtr1 Alkali trachyte and basalt flows, rhyolitic ignimbrite, minor tuff and bassal flows 17 NMt Teltele basalt flows 18 PNi Ignimbrite, minore tuff and bassalt 19 PNb2 Ankaramite and minor divine-phyric basalt 20 PNb1 Lower flood basalts 21 Tsy Hornblende-Alkali syenite, minor hornblende-nepheline syenite
Mesozoic Sedimentary Successions 22 Ka Amba Aradam Formation: varicoloured sandstones with inter beddding 23 Km Mustahil Formation: limestones inerbedded with shales and marls 24 Kg2 dominantly gypsum and anhydraites with beds of limestones, shales, marl and iron carbonate rock Korahe Formation: Lower unit dominantly sandstones with bes of dolomites, limestones marl, shale, 25 Kg1 gypsum and anhydrites 26 Jg Gabredarre Formation: micritic to microcrysaline and oolitic limestones
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27 Jh2 Hamanlei Formation: micritic locally oolitic (grainstone) peletic limestones 28 Jh1 Hamanlei Formation: less fossiliferous limestones with beds of calcareous sandstone Adigrat formation: variegated quatzos sanstones, intercalations of siltstones, shales and 29 Ja intrafomational conglomerates
Precambrean- Early paleozoic Basement Cmplexes 30 pcgt2 Blotite and homblende granites 31 pcdt Meta-quartz diorite plutonic bodies 32 pcgb1 Metagabbro 33 pcgd Metagranodiorite 34 pcgt1 Biotite metagranite 35 pcdt2 Quartz metadiorite 36 pcdt1 Melka Guga diorite gneiss
Mafic-Ultramafic-Volcano-Sedimentary Assemblages 37 pckb Kajimiti Beds: Metasandstone and metaconglomerate Metasediments: philite, metasiltstone melasandstone,micaschists, quartz-graphite-muscovite, 38 pcsc3 kyanite-muscovit schists 39 pcsc2 Metavolcanics: Amhibotite and plagioclase-chlorite-actinitit schist 40 pcsc1 Subvolcanic amphibolite 41 pcum2 Metaomblendite 42 pcgb2 metagabbro 43 pcum4 Talc, tremolite-Chiorite-talc, chlorite, chlorite-actindite and actinolite schists 44 pcum3 Serpentinite 45 pcum1 Undeferentiated metaltramafics
Gneissic and Migmataitic Complexes 46 pcgn11 Quartz-graphite schist,minor marble and quartz-sericite schist 47 pcgn10 Biotite-microline-quartz and gamet-staurolite -gneises and amphibolite 48 pcgn9 Biotite-quartz - oligoiase gneiss, amphibolite and digoclase-quartz-microcline gneiss 49 pcgn8 strongly migmatized-bioite-quartz-felspar gneiss(paragneisses) 50 pcgn7 Oligoclase-hornblende-biotite-quartz, biotite-hornblende, biotite and calcsilicate 51 pcgn6 Quartzofeldspathic gneiss, minor biotitie-feldspar-quartz gneiss and biotite granite pods 52 pcgn5 Magetite-quartzofeldspathic gneiss 53 pcgn4 wadera mylonite and mylonitic gneiss 54 pcgn3 Hornblende-biotite-quartz-feldspar and biotite-quartz-feldspar gneisses 55 pcgn2 Biotite-hornblende gneiss 56 pcgn1 Granulite-quartzofeldspathic gneiss
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Appendix 4 Mat lab Code for filling missing rainfall data
(Using inverse distance method) clear clc dims(:,:,1)=load('Abissa.dat'); dims(:,:,2)=load('Agafara.dat'); dims(:,:,3)=load('Aleta Wendo.dat'); dims(:,:,4)=load('Amaro Kello.dat'); dims(:,:,5)=load('Arsi Negele.dat'); dims(:,:,6)=load('Asahara.dat'); dims(:,:,7)=load('Berra.dat'); dims(:,:,8)=load('Bidere.dat'); dims(:,:,9)=load('Bulbula.dat'); dims(:,:,10)=load('Bulle.dat'); dims(:,:,11)=load('Dadime.dat'); dims(:,:,12)=load('Dello Mena.dat'); dims(:,:,13)=load('Delo Sebro.dat'); dims(:,:,14)=load('Dilla.dat'); dims(:,:,15)=load('Edo_Dodola.dat'); dims(:,:,16)=load('Filtu2.dat'); dims(:,:,17)=load('Finch Wuha.dat'); dims(:,:,18)=load('Fiseha Genet.dat'); dims(:,:,19)=load('Gedebe.dat'); dims(:,:,20)=load('Genale Donta.dat'); dims(:,:,21)=load('Gesera.dat'); dims(:,:,22)=load('Gobessa III.dat'); dims(:,:,23)=load('Indento.dat'); dims(:,:,24)=load('Kebado.dat'); dims(:,:,25)=load('Konso.dat'); dims(:,:,26)=load('Mega.dat'); dims(:,:,27)=load('Melka Odda1.dat'); dims(:,:,28)=load('Oddo Shakiso.dat'); dims(:,:,29)=load('Sofomor.dat'); dims(:,:,30)=load('Teferekella.dat'); dims(:,:,31)=load('Telamo Kentiso.dat'); dims(:,:,32)=load('Ticho.dat'); dims(:,:,33)=load('Tuka.dat'); dims(:,:,34)=load('Wadera.dat'); dims(:,:,35)=load('Yirga Chefe.dat'); format longG n=size(dims,1); m=size(dims,2); q=size(dims,3); Nebiyou k Page 72
Groundwater potential assessment and characterization of Genale-Dawa River basin los=0; for j=1:n; for p=1:q; % for p=16; for k=1:m; if dims(j,k,p)==999; % 4 is a random point for the selection of coordinate points x and % y % u is coordinate of neigboring station and v is missed station u(:,:)=dims(4,[1,2],:); v=dims(4,[1,2],p); w=setdiff(u',v,'rows'); nd=3; [Neighbors,distance] = kNearestNeighbors(w,v,nd); % obtaining indexes x=Neighbors(1,1); y=Neighbors(1,2); z=Neighbors(1,3); % using the layers select the layer to work on lx=dims(:,:,x); ly=dims(:,:,y); lz=dims(:,:,z); % extract index corrosponding to missing data
[x1,x2]=ind2sub(n,find((lx(1:n,4)==dims(j,4,p)))); [xd,xd2]=ind2sub(n,find((lx(1:n,5)==dims(j,5,p)))); xf=intersect(x1,xd);
[y1,y2]=ind2sub(300,find((ly(1:n,4)==dims(j,4,p)))); [yd,yd2]=ind2sub(300,find((ly(1:n,5)==dims(j,5,p)))); yf=intersect(y1,yd);
[z1,z2]=ind2sub(300,find((lz(1:n,4)==dims(j,4,p)))); [zd,zd2]=ind2sub(300,find((lz(1:n,5)==dims(j,5,p)))); zf=intersect(z1,zd); % looging for other neareast stations with desired record loop1=0;
ys =[]; xs =[];
while isempty(xf)||dims(xf,k,x)==999; lop1=0; u1=dims(4,[1,2],x); u2=dims(4,[1,2],y); u3=dims(4,[1,2],z); v1(:,:)=dims(4,[1,2],p); u10=cat(1,u1,u2,u3,v1); % u4=union(u10,u2,'rows'); u30(:,:)=dims(4,[1,2],:); w1=setdiff(u30',u10,'rows');
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while isempty(xf)||dims(xf,k,x)==999;
v5=setdiff(w1,u10,'rows');
[Neighbors(1,1),distance(1,1)] = kNearestNeighbors(v5,v1,1); x=Neighbors(1,1); u100=u30'; % lx=v5(x,[1,2])==u100(:,:);
[xs1,xs2]=ind2sub(300,find(u100(1:size(u100),1)==v5(x,1))); [xc,xc2]=ind2sub(300,find(u100(1:size(u100),2)==v5(x,2))); xs=intersect(xs1,xc); lx=dims(:,:,xs);
[x1,x2]=ind2sub(300,find((lx(1:n,4)==dims(j,4,p)))); [xd,xd2]=ind2sub(300,find((lx(1:n,5)==dims(j,5,p)))); % [xe,xde2]=ind2sub(300,find((lx(1:n,[1,2])==dims(4,[1,2],:))));
xf=intersect(x1,xd); u10=cat(1,u10,dims(4,[1,2],xs)); x=xs; lop1= lop1+1; % u4=union(u4,(dims(4,[1,2],x)),'rows'); if lop1==(q-5),break,end end loop1= loop1+1; if loop1==1,break,end end
lop2=0;
while isempty(yf)||dims(yf,k,y)==999;
loop2=0; u4=dims(4,[1,2],x); u5=dims(4,[1,2],y); u6=dims(4,[1,2],z); v2(:,:)=dims(4,[1,2],p); % xxs=dims(4,[1,2],xs); if isempty (xs) u11=cat(1,u4,u5,u6,v2); else u11=cat(1,u4,u5,u6,v2,dims(4,[1,2],xs)); end % u11=cat(1,u4,u5,u6,v2,dims(4,[1,2],xf)); u31(:,:)=dims(4,[1,2],:); w2=setdiff(u31',u11,'rows'); % v2(:,:)=dims(4,[1,2],p);
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while isempty(yf)||dims(yf,k,y)==999;
v3=setdiff(w2,u11,'rows');
[Neighbors(1,2),distance(1,2)] = kNearestNeighbors(v3,v2,1); Y=Neighbors(1,2); % ly=dims(:,:,y);
u101=u31';
[Ys1,Ys2]=ind2sub(300,find(u101(1:size(u101),1)==v3(Y,1))); [Yc,Yc2]=ind2sub(300,find(u101(1:size(u101),2)==v3(Y,2)));
ys=intersect(Ys1,Yc); ly=dims(:,:,ys);
y=ys; [y1,y2]=ind2sub(300,find((ly(1:n,4)==dims(j,4,p)))); [yd,yd2]=ind2sub(300,find((ly(1:n,5)==dims(j,5,p)))); yf=intersect(y1,yd);
loop2= loop2+1; u11=cat(1,u11,(dims(4,[1,2],ys))); if loop2==(q-5),break,end
end lop2= lop2+1; if lop2==1,break,end end
lop3=0; while isempty(zf)||dims(zf,k,z)==999; loop3=0;
u7=dims(4,[1,2],x); u8=dims(4,[1,2],y); u9=dims(4,[1,2],z); v4(:,:)=dims(4,[1,2],p);
if isempty(ys) && isempty (xs); u12=cat(1,u7,u8,u9,v4); elseif isempty (ys); u12=cat(1,u7,u8,u9,v4,dims(4,[1,2],xs)); elseif isempty(xs); u12=cat(1,u7,u8,u9,v4,dims(4,[1,2],ys)); elseif ~isempty(ys) && ~isempty (xs); u12=cat(1,u7,u8,u9,v4,dims(4,[1,2],xs),dims(4,[1,2],ys)); end % u12=cat(1,u7,u8,u9,v4,dims(4,[1,2],xf),dims(4,[1,2],yf)); % dims(4,[1,2],xs),dims(4,[1,2],Ys) u32(:,:)=dims(4,[1,2],:);
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w3=setdiff(u32',u12,'rows'); % v7(:,:)=dims(4,[1,2],p);
while isempty(zf)||dims(zf,k,z)==999; % u1(:,:)=dims(4,[1,2],:); % w3=setdiff(u1',u2,'rows'); % v7(:,:)=dims(4,[1,2],p); v7=setdiff(w3,u12,'rows');
[Neighbors(1,3),distance(1,3)] = kNearestNeighbors(v7,v4,1); z=Neighbors(1,3);
u102=u32';
[zs1,zs2]=ind2sub(300,find(u102(1:size(u102),1)==v7(z,1))); [zc,zc2]=ind2sub(300,find(u102(1:size(u102),2)==v7(z,2)));
zs=intersect(zs1,zc);
lz=dims(:,:,zs);
[z1,z2]=ind2sub(300,find((lz(1:n,4)==dims(j,4,p)))); [zd,zd2]=ind2sub(300,find((lz(1:n,5)==dims(j,5,p)))); zf=intersect(z1,zd); u12=cat(1,u12,dims(4,[1,2],zs)); z=zs; loop3= loop3+1; % u12=union(u12,(dims(4,[1,2],zs)),'rows'); if loop3==(q-5),break,end
end lop3= lop3+1; if lop3==1,break,end end
% Calculation by linear inverse distance for the respective conditions if isempty(xf)&&(~isempty(zf)&&~isempty(yf)); if(((dims(zf,k,z)==999 || dims(yf,k,y)==999))); if dims(zf,k,z)==999 dims(j,k,p)=dims(yf,k,y); elseif dims(yf,k,y)==999 dims(j,k,p)=dims(zf,k,z); end elseif(((dims(zf,k,z)==999 && dims(yf,k,y)==999))); dims(j,k,p)=999; elseif(((dims(zf,k,z)~=999 && dims(yf,k,y)~=999))); dims(j,k,p)=1/(1/distance(1,2)+1/distance(1,3))*((dims(zf,k,z)/distance(1,3)) + (dims(yf,k,y)/distance(1,2))); end elseif isempty(yf)&&(~isempty(zf)&&~isempty(xf));
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if(((dims(xf,k,x)==999 || dims(zf,k,z)==999))); if dims(xf,k,x)==999 dims(j,k,p)=dims(zf,k,z); elseif dims(zf,k,z)==999 dims(j,k,p)=dims(xf,k,x); end elseif(((dims(xf,k,x)==999 && dims(zf,k,z)==999))) dims(j,k,p)=999; elseif(((dims(xf,k,x)~=999 && dims(zf,k,z)~=999))); dims(j,k,p)=1/(1/distance(1,3)+1/distance(1,1))*((dims(xf,k,x)/distance(1,1)) + (dims(zf,k,z)/distance(1,3))); end elseif isempty(zf)&&(~isempty(xf)&&~isempty(yf)); if(((dims(xf,k,x)==999 || dims(yf,k,y)==999))); if dims(xf,k,x)==999 dims(j,k,p)=dims(yf,k,y); elseif dims(yf,k,y)==999 dims(j,k,p)=dims(xf,k,x); end elseif(((dims(xf,k,x)==999 && dims(yf,k,y)==999))); dims(j,k,p)=999; elseif(((dims(xf,k,x)~=999 && dims(yf,k,y)~=999))); dims(j,k,p)=1/(1/distance(1,2)+1/distance(1,1))*((dims(xf,k,x)/distance(1,1)) + (dims(yf,k,y)/distance(1,2))); end elseif ((isempty(xf) && isempty(zf))&& isempty(yf)); dims(j,k,p)=999; elseif ((isempty(xf) && isempty(zf))||(isempty(xf) && isempty(yf))||(isempty(zf) && isempty(yf))); if(isempty(xf) && isempty(zf)); dims(j,k,p)=dims(yf,k,y); elseif (isempty(xf) && isempty(yf)); dims(j,k,p)=dims(zf,k,z); else dims(j,k,p)=dims(xf,k,x); end else if dims(zf,k,z)==999 && (dims(xf,k,x)~=999 && dims(yf,k,y)~=999) dims(j,k,p)=1/(1/distance(1,1)+1/distance(1,2))*((dims(xf,k,x)/distance(1,1)) + (dims(yf,k,y)/distance(1,2)));
elseif dims(xf,k,x)==999 && (dims(zf,k,z)~=999 && dims(yf,k,y)~=999) dims(j,k,p)=1/(1/distance(1,2)+1/distance(1,3))*((dims(yf,k,y)/distance(1,2))+ (dims(zf,k,z)/distance(1,3)));
elseif dims(yf,k,y)==999 && (dims(zf,k,z)~=999 && dims(xf,k,x)~=999)
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dims(j,k,p)=1/(1/distance(1,1)+1/distance(1,3))*((dims(xf,k,x)/distance(1,1)) +(dims(zf,k,z)/distance(1,3)));
elseif (dims(zf,k,z)==999 && dims(xf,k,x)==999)||(dims(zf,k,z)==999 && dims(yf,k,y)==999)||(dims(yf,k,y)==999 && dims(xf,k,x)==999) dims(j,k,p)=((dims(xf,k,x)+dims(yf,k,y)+dims(zf,k,z))- (2*999)); elseif dims(zf,k,z)~=999 && (dims(xf,k,x)~=999 && dims(yf,k,y)~=999) dims(j,k,p)=1/(1/distance(1,1)+1/distance(1,2)+1/distance(1,3))*((dims(xf,k,x)/distance(1,1))+(di ms(yf,k,y)/distance(1,2)) +(dims(zf,k,z)/distance(1,3))); else dims(j,k,p)=1/(1/distance(1,1)+1/distance(1,2)+1/distance(1,3))*((dims(xf,k,x)/distance(1,1))+(di ms(yf,k,y)/distance(1,2)) +(dims(zf,k,z)/distance(1,3))); end
end
else dims(j,k,p)= dims(j,k,p); end end end end % los=los+1; % if los==2,break,end % end % store the matrix in diffrent variables Abissa=dims(:,:,1); Agafara=dims(:,:,2); AletaWendo=dims(:,:,3); AmaroKello=dims(:,:,4); ArsiNegele=dims(:,:,5); Asahara=dims(:,:,6); Berra=dims(:,:,7); Bidere=dims(:,:,8); Bulbula=dims(:,:,9); Bulle=dims(:,:,10); Dadime=dims(:,:,11); DelloMena=dims(:,:,12); DeloSebro=dims(:,:,13); Dilla=dims(:,:,14); Edo_Dodola=dims(:,:,15); Filtu=dims(:,:,16); FinchWuha=dims(:,:,17); FisehaGenet=dims(:,:,18); Gedebe=dims(:,:,19); GenaleDonta=dims(:,:,20);
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Gesera=dims(:,:,21); GobessaIII=dims(:,:,22); Indento=dims(:,:,23); Kebado=dims(:,:,24); Konso=dims(:,:,25); Mega=dims(:,:,26); MelkaOdda=dims(:,:,27); OddoShakiso=dims(:,:,28); Sofomor=dims(:,:,29); Teferekella=dims(:,:,30); TelamoKentiso=dims(:,:,31); Ticho=dims(:,:,32); Tuka=dims(:,:,33); Wadera=dims(:,:,34); YirgaChefe=dims(:,:,35);
% save data in separate sheet
save Abissa1.dat Abissa -ascii save Agafara1.dat Agafara -ascii save AletaWendo1.dat AletaWendo -ascii save AmaroKello1.dat AmaroKello -ascii save ArsiNegele1.dat ArsiNegele -ascii save Asahara1.dat Asahara -ascii save Berra1.dat Berra -ascii save Bidere1.dat Bidere -ascii save Bulbula1.dat Bulbula -ascii save Bulle1.dat Bulle -ascii save Dadime1.dat Dadime -ascii save DelloMena1.dat DelloMena -ascii save DeloSebro1.dat DeloSebro -ascii save Dilla1.dat Dilla -ascii save Edo_Dodola1.dat Edo_Dodola -ascii save Filtu1.dat Filtu -ascii save FinchWuha1.dat FinchWuha -ascii save FisehaGenet1.dat FisehaGenet -ascii save Gedebe1.dat Gedebe -ascii save GenaleDonta1.dat GenaleDonta -ascii save Gesera1.dat Gesera -ascii save GobessaIII1.dat GobessaIII -ascii save Indento1.dat Indento -ascii save Kebado1.dat Kebado -ascii save Konso1.dat Konso -ascii save Mega1.dat Mega -ascii save MelkaOdda11.dat MelkaOdda -ascii save OddoShakiso1.dat OddoShakiso -ascii save Sofomor1.dat Sofomor -ascii save Teferekella1.dat Teferekella -ascii save TelamoKentiso1.dat TelamoKentiso -ascii save Ticho1.dat Ticho -ascii save Tuka1.dat Tuka -ascii save Wadera1.dat Wadera -ascii save YirgaChefe1.dat YirgaChefe -ascii
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